This paper presents a mid-level planning system for object reorientation. It includes a grasp pla... more This paper presents a mid-level planning system for object reorientation. It includes a grasp planner, a placement planner, and a regrasp sequence solver. Given the initial and goal poses of an object, the mid-level planning system finds a sequence of hand configurations that reorient the object from the initial to the goal. This mid-level planning system is open to low-level motion planning algorithm by providing two endeffector poses as the input. It is also open to high-level symbolic planners by providing interface functions like placing an object to a given position at a given rotation. The planning system is demonstrated with several simulation examples and real-robot executions using a Kawada Hiro robot and Robotiq 85 grippers.
This paper presents 1)reliminary rcsults on generat ． ing tuming rIlotion of a hllmalloid robot b... more This paper presents 1)reliminary rcsults on generat ． ing tuming rIlotion of a hllmalloid robot based oII human motion data obtained by using motioIl capturing system ． Thc target h し manoid robot is recently released HRP − 4C ， looks likc a Japanese woman with a realistic geolnetry ． Proposed method to geuerate turn Inotion Qf a hunlanoid robot contains a lot of kllow − how such as dctection of f ot landiIlg ， modification of waist a 皿 gle and feet position ． Veri丘cation is conducted through both simulatioIl a 皿 d experiment wi む h the hulllanoid robot HRP − 4C ． A turniIlg rnotion based on hulnan motion is succesg ． fully delIlonstrated ，
This paper proposes a new framework for planning assembly tasks involving elastic parts. As an ex... more This paper proposes a new framework for planning assembly tasks involving elastic parts. As an example of these kind of assembly tasks, we deal with the insertion of ring-shaped objects into a cylinder by a dual-arm robot. The proposed framework is a combination of human movements to determine the overall assembly strategy and an optimization-based motion planner. The motion of the human's hands, more specifically, the motion of the fingers gripping the object is captured by a Leap Motion Controller. Then, key points in the recorded trajectory of the position and orientation of the human's fingers are extracted. These points are used as partial goals in the optimization-based motion planner that generates the robot arms' trajectories minimizing the object's deformation. Through experimental results it was verified the validity of the proposed framework.
This paper presents a brief review of affordance research in robotics, with special concentration... more This paper presents a brief review of affordance research in robotics, with special concentrations on its applications in grasping and manipulation of objects. The concept of affordance could be a key to realize human-like advanced manipulation intelligence. First, we discuss the concept of affordance while associating with the applications in robotics. Then, we intensively explore the studies that utilize affordance for robotic manipulation applications, such as object recognition, grasping, and object manipulation including tool-use. They obtain and use affordance by several ways like learning from human, using simulation, and real-world execution. Moreover, we show our current work, which is a cloud database for advanced manipulation intelligence. The database accumulates various data related to manipulation task execution and will be an open platform to leverage various affordance techniques.
This paper proposes a combined task and motion planner for a dual-arm robot to use a suction cup ... more This paper proposes a combined task and motion planner for a dual-arm robot to use a suction cup tool. The planner consists of three sub-planners-A suction pose subplanner and two regrasp and motion sub-planners. The suction pose sub-planner finds all the available poses for a suction cup tool to suck on the object, using the models of the tool and the object. The regrasp and motion sub-planner builds the regrasp graph that represents all possible grasp sequences to reorient and move the suction cup tool from an initial pose to a goal pose. Two regrasp graphs are used to plan for a single suction cup and the complex of the suction cup and an object respectively. The output of the proposed planner is a sequence of robot motion that uses a suction cup tool to manipulate objects following human instructions. The planner is examined and analyzed by both simulation experiments and real-world executions using several real-world tasks. The results show that the planner is efficient, robust, and can generate sequential transit and transfer robot motion to finish complicated combined task and motion planning tasks in a few seconds.
Complex and skillful motions in actual assembly process are challenging for the robot to generate... more Complex and skillful motions in actual assembly process are challenging for the robot to generate with existing motion planning approaches, because some key poses during the human assembly can be too skillful for the robot to realize automatically. In order to deal with this problem, this paper develops a motion planning method using skillful motions from demonstration, which can be applied to complete robotic assembly process including complex and skillful motions. In order to demonstrate conveniently without redundant thirdparty devices, we attach augmented reality (AR) markers to the manipulated object to track and capture poses of the object during the human assembly process, which are employed as key poses to execute motion planning by the planner. Derivative of every key pose serves as criterion to determine the priority of use of key poses in order to accelerate the motion planning. The effectiveness of the presented method is verified through some numerical examples and actual robot experiments.
A robot manipulation system that separates and arranges test tubes in racks with the help of 3D v... more A robot manipulation system that separates and arranges test tubes in racks with the help of 3D vision and artificial intelligence (AI) reasoning/planning. MAIN TEXT The large amount of infections by COVID-19 drives people to perform thousands of polymerase chain reaction tests, antibody tests, etc. These tests require to handle a huge amount of test tubes, which are not only labor-intensive but also pressing. Employing a simple-to-use robot to do the job and consequently replace human labor is highly expected.
To realize a dense object placement into a container, we propose a robotic packing motion planner... more To realize a dense object placement into a container, we propose a robotic packing motion planner by pushing objects to the side of other objects. Our method comprises three planning strategies, i.e., object placement planning, robotic packing-action planning, and action sequence planning. Object placement planning generates objects’ placement into a container without gaps between objects. Based on the planned placement, the robotic packing-action planner selectively uses two action strategies where one is to directly place the object in the desired location of a container by using a pick-andplace approach, and the other is to first place the object at a certain distance from the surrounding object and then push it to achieve the placement without gaps. Finally, the action sequence planning plans the order of selected manipulation strategies. Through experiments, we confirmed that the robot efficiently packs multiple objects into a container by effectively using object pushing.
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Pick-and-place regrasp extends the manipulation capability of a robot by using a sequence of regr... more Pick-and-place regrasp extends the manipulation capability of a robot by using a sequence of regrasps to accomplish tasks that are not possible using a single grasp due to constraints such as kinematics or collisions between the robot and the environment. Previous work on pick-and-place only leveraged static passive devices for intermediate placements, and thus is limited in the flexibility and robustness to reorient an object. In this paper, we extend the reorientation capability of a pick-and-place regrasp by adding an actively actuated gripper fixed in the working cell, and using it as the intermediate location for regrasping. In particular, our method automatically computes the stable placements of an object being hold in the gripper support, finds a rich set of force-closure grasps, performs k-means based grasp clustering, generates a graph of regrasp actions, and searches for the optimal regrasp sequence. To compare the regrasping performance with typical passive supports, we evaluate the success rate while performing tasks on various models. Experiments on reorientation tasks validate the benefit of using an actively actuated gripper for regrasp placement.
Wire harnesses are essential connecting components in manufacturing industry but are challenging ... more Wire harnesses are essential connecting components in manufacturing industry but are challenging to be automated in industrial tasks such as bin picking. They are long, flexible and tend to get entangled when randomly placed in a bin. This makes it difficult for the robot to grasp a single one in dense clutter. Besides, training or collecting data in simulation is challenging due to the difficulties in modeling the combination of deformable and rigid components for wire harnesses. In this work, instead of directly lifting wire harnesses, we propose to grasp and extract the target following a circle-like trajectory until it is untangled. We learn a policy from real-world data that can infer grasps and separation actions from visual observation. Our policy enables the robot to efficiently pick and separate entangled wire harnesses by maximizing success rates and reducing execution time. To evaluate our policy, we present a set of real-world experiments on picking wire harnesses. Our policy achieves an overall 84.6% success rate compared with 49.2% in baseline. We also evaluate the effectiveness of our policy under different clutter scenarios using unseen types of wire harnesses. Results suggest that our approach is feasible for handling wire harnesses in industrial bin picking. Supplementary material, code, and videos can be found at https://xinyiz0931.github.io/aspnet.
2019 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO)
This paper presents a constrained motion planning to flip objects grasped considering soft-finger... more This paper presents a constrained motion planning to flip objects grasped considering soft-finger contact while avoiding rotational slip (or namely drooping). The drooping motion in inclined planes is studied under the effect of the object weight and the friction torque exerted by soft fingertips. The analytical model of the drooping motion is derived and used in the proposed constrained planner to restrict the robot poses that can cause the object held by the gripper to slip. The constrained planner is analyzed and verified through both simulation and real-world experiments. The results show that the generated robot motion sequences enable correct execution of the flipping task while avoiding slip.
This paper presents a combined task and motion planner for a robot arm to carry out 3D metal wire... more This paper presents a combined task and motion planner for a robot arm to carry out 3D metal wire curving tasks by collaborating with a bending machine. We assume a collaborative robot that is safe to work in a human environment but has a weak payload to bend objects with large stiffness, and developed a combined planner for the robot to use a bending machine. Our method converts a 3D curve to a bending set and generates the feasible bending sequence, machine usage, robotic grasp poses, and pick-and-place arm motion considering the combined task and motion level constraints. Compared with previous deformable linear object shaping work that relied on forces provided by robotic arms, the proposed method is suitable for the material with high stiffness. We evaluate the system using different tasks. The results show that the proposed system is flexible and robust to generate robotic motion to corporate with the designed bending machine.
Proceedings of International Conference on Artificial Life and Robotics, 2022
It is challenging to retrieve a target object from a randomly stacked pile by using a robot due t... more It is challenging to retrieve a target object from a randomly stacked pile by using a robot due to the occlusion of the target object. In this study, we propose a novel retrieval method in which a robot selects the viewpose to observe the occlusion part of the target object using the RGB-D images, and then selects the motion of grasping/dragging to retrieve the object depending on the configuration of the pile. We experimentally confirm that a robot effectively observes a pile with a complex configuration and successfully retrieves a target object.
2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), 2018
This paper presents a double jaw hand for industrial assembly. The hand comprises two orthogonal ... more This paper presents a double jaw hand for industrial assembly. The hand comprises two orthogonal parallel grippers with different mechanisms. The inner gripper is made of a crank-slider mechanism which is compact and able to firmly hold objects like shafts. The outer gripper is made of a parallelogram that has large stroke to hold big objects like pulleys. The two grippers are connected by a prismatic joint along the hand's approaching vector. The hand is able to hold two objects and perform in-hand manipulation like pull-in (insertion) and push-out (ejection). This paper presents the detailed design and implementation of the hand, and demonstrates the advantages by performing experiments on two sets of peg-in-multi-hole assembly tasks as parts of the World Robot Challenge (WRC) 2018 a using a bimanual robot. Index Terms-Assembly, grippers, grasping, in-hand manipulation.
2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM), 2018
We present bilateral teleoperation system for task learning and robot motion generation. Our syst... more We present bilateral teleoperation system for task learning and robot motion generation. Our system includes a bilateral teleoperation platform and a deep learning software. The deep learning software refers to human demonstration using the bilateral teleoperation platform to collect visual images and robotic encoder values. It leverages the datasets of images and robotic encoder information to learn about the intermodal correspondence between visual images and robot motion. In detail, the deep learning software uses a combination of Deep Convolutional Auto-Encoders (DCAE) over image regions, and Recurrent Neural Network with Long Short-Term Memory units (LSTM-RNN) over robot motor angles, to learn motion taught be human teleoperation. The learnt models are used to predict new motion trajectories for similar tasks. Experimental results show that our system has the adaptivity to generate motion for similar scooping tasks. Detailed analysis is performed based on failure cases of the experimental results. Some insights about the cans and cannots of the system are summarized.
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
Planning dual-arm assembly of more than three objects is a challenging Task and Motion Planning (... more Planning dual-arm assembly of more than three objects is a challenging Task and Motion Planning (TAMP) problem. The assembly planner shall consider not only the pose constraints of objects and robots, but also the gravitational constraints that may break the finished part. This paper proposes a planner to plan the dual-arm assembly of more than three objects. It automatically generates the grasp configurations and assembly poses, and simultaneously searches and backtracks the grasp space and assembly space to accelerate the motion planning of robot arms. Meanwhile, the proposed method considers gravitational constraints during robot motion planning to avoid breaking the finished part. In the experiments and analysis section, the time cost of each process and the influence of different parameters used in the proposed planner are compared and analyzed. The optimal values are used to perform real-world executions of various robotic assembly tasks. The planner is proved to be robust and efficient through the experiments.
This paper presents a mid-level planning system for object reorientation. It includes a grasp pla... more This paper presents a mid-level planning system for object reorientation. It includes a grasp planner, a placement planner, and a regrasp sequence solver. Given the initial and goal poses of an object, the mid-level planning system finds a sequence of hand configurations that reorient the object from the initial to the goal. This mid-level planning system is open to low-level motion planning algorithm by providing two endeffector poses as the input. It is also open to high-level symbolic planners by providing interface functions like placing an object to a given position at a given rotation. The planning system is demonstrated with several simulation examples and real-robot executions using a Kawada Hiro robot and Robotiq 85 grippers.
This paper presents 1)reliminary rcsults on generat ． ing tuming rIlotion of a hllmalloid robot b... more This paper presents 1)reliminary rcsults on generat ． ing tuming rIlotion of a hllmalloid robot based oII human motion data obtained by using motioIl capturing system ． Thc target h し manoid robot is recently released HRP − 4C ， looks likc a Japanese woman with a realistic geolnetry ． Proposed method to geuerate turn Inotion Qf a hunlanoid robot contains a lot of kllow − how such as dctection of f ot landiIlg ， modification of waist a 皿 gle and feet position ． Veri丘cation is conducted through both simulatioIl a 皿 d experiment wi む h the hulllanoid robot HRP − 4C ． A turniIlg rnotion based on hulnan motion is succesg ． fully delIlonstrated ，
This paper proposes a new framework for planning assembly tasks involving elastic parts. As an ex... more This paper proposes a new framework for planning assembly tasks involving elastic parts. As an example of these kind of assembly tasks, we deal with the insertion of ring-shaped objects into a cylinder by a dual-arm robot. The proposed framework is a combination of human movements to determine the overall assembly strategy and an optimization-based motion planner. The motion of the human's hands, more specifically, the motion of the fingers gripping the object is captured by a Leap Motion Controller. Then, key points in the recorded trajectory of the position and orientation of the human's fingers are extracted. These points are used as partial goals in the optimization-based motion planner that generates the robot arms' trajectories minimizing the object's deformation. Through experimental results it was verified the validity of the proposed framework.
This paper presents a brief review of affordance research in robotics, with special concentration... more This paper presents a brief review of affordance research in robotics, with special concentrations on its applications in grasping and manipulation of objects. The concept of affordance could be a key to realize human-like advanced manipulation intelligence. First, we discuss the concept of affordance while associating with the applications in robotics. Then, we intensively explore the studies that utilize affordance for robotic manipulation applications, such as object recognition, grasping, and object manipulation including tool-use. They obtain and use affordance by several ways like learning from human, using simulation, and real-world execution. Moreover, we show our current work, which is a cloud database for advanced manipulation intelligence. The database accumulates various data related to manipulation task execution and will be an open platform to leverage various affordance techniques.
This paper proposes a combined task and motion planner for a dual-arm robot to use a suction cup ... more This paper proposes a combined task and motion planner for a dual-arm robot to use a suction cup tool. The planner consists of three sub-planners-A suction pose subplanner and two regrasp and motion sub-planners. The suction pose sub-planner finds all the available poses for a suction cup tool to suck on the object, using the models of the tool and the object. The regrasp and motion sub-planner builds the regrasp graph that represents all possible grasp sequences to reorient and move the suction cup tool from an initial pose to a goal pose. Two regrasp graphs are used to plan for a single suction cup and the complex of the suction cup and an object respectively. The output of the proposed planner is a sequence of robot motion that uses a suction cup tool to manipulate objects following human instructions. The planner is examined and analyzed by both simulation experiments and real-world executions using several real-world tasks. The results show that the planner is efficient, robust, and can generate sequential transit and transfer robot motion to finish complicated combined task and motion planning tasks in a few seconds.
Complex and skillful motions in actual assembly process are challenging for the robot to generate... more Complex and skillful motions in actual assembly process are challenging for the robot to generate with existing motion planning approaches, because some key poses during the human assembly can be too skillful for the robot to realize automatically. In order to deal with this problem, this paper develops a motion planning method using skillful motions from demonstration, which can be applied to complete robotic assembly process including complex and skillful motions. In order to demonstrate conveniently without redundant thirdparty devices, we attach augmented reality (AR) markers to the manipulated object to track and capture poses of the object during the human assembly process, which are employed as key poses to execute motion planning by the planner. Derivative of every key pose serves as criterion to determine the priority of use of key poses in order to accelerate the motion planning. The effectiveness of the presented method is verified through some numerical examples and actual robot experiments.
A robot manipulation system that separates and arranges test tubes in racks with the help of 3D v... more A robot manipulation system that separates and arranges test tubes in racks with the help of 3D vision and artificial intelligence (AI) reasoning/planning. MAIN TEXT The large amount of infections by COVID-19 drives people to perform thousands of polymerase chain reaction tests, antibody tests, etc. These tests require to handle a huge amount of test tubes, which are not only labor-intensive but also pressing. Employing a simple-to-use robot to do the job and consequently replace human labor is highly expected.
To realize a dense object placement into a container, we propose a robotic packing motion planner... more To realize a dense object placement into a container, we propose a robotic packing motion planner by pushing objects to the side of other objects. Our method comprises three planning strategies, i.e., object placement planning, robotic packing-action planning, and action sequence planning. Object placement planning generates objects’ placement into a container without gaps between objects. Based on the planned placement, the robotic packing-action planner selectively uses two action strategies where one is to directly place the object in the desired location of a container by using a pick-andplace approach, and the other is to first place the object at a certain distance from the surrounding object and then push it to achieve the placement without gaps. Finally, the action sequence planning plans the order of selected manipulation strategies. Through experiments, we confirmed that the robot efficiently packs multiple objects into a container by effectively using object pushing.
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Pick-and-place regrasp extends the manipulation capability of a robot by using a sequence of regr... more Pick-and-place regrasp extends the manipulation capability of a robot by using a sequence of regrasps to accomplish tasks that are not possible using a single grasp due to constraints such as kinematics or collisions between the robot and the environment. Previous work on pick-and-place only leveraged static passive devices for intermediate placements, and thus is limited in the flexibility and robustness to reorient an object. In this paper, we extend the reorientation capability of a pick-and-place regrasp by adding an actively actuated gripper fixed in the working cell, and using it as the intermediate location for regrasping. In particular, our method automatically computes the stable placements of an object being hold in the gripper support, finds a rich set of force-closure grasps, performs k-means based grasp clustering, generates a graph of regrasp actions, and searches for the optimal regrasp sequence. To compare the regrasping performance with typical passive supports, we evaluate the success rate while performing tasks on various models. Experiments on reorientation tasks validate the benefit of using an actively actuated gripper for regrasp placement.
Wire harnesses are essential connecting components in manufacturing industry but are challenging ... more Wire harnesses are essential connecting components in manufacturing industry but are challenging to be automated in industrial tasks such as bin picking. They are long, flexible and tend to get entangled when randomly placed in a bin. This makes it difficult for the robot to grasp a single one in dense clutter. Besides, training or collecting data in simulation is challenging due to the difficulties in modeling the combination of deformable and rigid components for wire harnesses. In this work, instead of directly lifting wire harnesses, we propose to grasp and extract the target following a circle-like trajectory until it is untangled. We learn a policy from real-world data that can infer grasps and separation actions from visual observation. Our policy enables the robot to efficiently pick and separate entangled wire harnesses by maximizing success rates and reducing execution time. To evaluate our policy, we present a set of real-world experiments on picking wire harnesses. Our policy achieves an overall 84.6% success rate compared with 49.2% in baseline. We also evaluate the effectiveness of our policy under different clutter scenarios using unseen types of wire harnesses. Results suggest that our approach is feasible for handling wire harnesses in industrial bin picking. Supplementary material, code, and videos can be found at https://xinyiz0931.github.io/aspnet.
2019 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO)
This paper presents a constrained motion planning to flip objects grasped considering soft-finger... more This paper presents a constrained motion planning to flip objects grasped considering soft-finger contact while avoiding rotational slip (or namely drooping). The drooping motion in inclined planes is studied under the effect of the object weight and the friction torque exerted by soft fingertips. The analytical model of the drooping motion is derived and used in the proposed constrained planner to restrict the robot poses that can cause the object held by the gripper to slip. The constrained planner is analyzed and verified through both simulation and real-world experiments. The results show that the generated robot motion sequences enable correct execution of the flipping task while avoiding slip.
This paper presents a combined task and motion planner for a robot arm to carry out 3D metal wire... more This paper presents a combined task and motion planner for a robot arm to carry out 3D metal wire curving tasks by collaborating with a bending machine. We assume a collaborative robot that is safe to work in a human environment but has a weak payload to bend objects with large stiffness, and developed a combined planner for the robot to use a bending machine. Our method converts a 3D curve to a bending set and generates the feasible bending sequence, machine usage, robotic grasp poses, and pick-and-place arm motion considering the combined task and motion level constraints. Compared with previous deformable linear object shaping work that relied on forces provided by robotic arms, the proposed method is suitable for the material with high stiffness. We evaluate the system using different tasks. The results show that the proposed system is flexible and robust to generate robotic motion to corporate with the designed bending machine.
Proceedings of International Conference on Artificial Life and Robotics, 2022
It is challenging to retrieve a target object from a randomly stacked pile by using a robot due t... more It is challenging to retrieve a target object from a randomly stacked pile by using a robot due to the occlusion of the target object. In this study, we propose a novel retrieval method in which a robot selects the viewpose to observe the occlusion part of the target object using the RGB-D images, and then selects the motion of grasping/dragging to retrieve the object depending on the configuration of the pile. We experimentally confirm that a robot effectively observes a pile with a complex configuration and successfully retrieves a target object.
2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), 2018
This paper presents a double jaw hand for industrial assembly. The hand comprises two orthogonal ... more This paper presents a double jaw hand for industrial assembly. The hand comprises two orthogonal parallel grippers with different mechanisms. The inner gripper is made of a crank-slider mechanism which is compact and able to firmly hold objects like shafts. The outer gripper is made of a parallelogram that has large stroke to hold big objects like pulleys. The two grippers are connected by a prismatic joint along the hand's approaching vector. The hand is able to hold two objects and perform in-hand manipulation like pull-in (insertion) and push-out (ejection). This paper presents the detailed design and implementation of the hand, and demonstrates the advantages by performing experiments on two sets of peg-in-multi-hole assembly tasks as parts of the World Robot Challenge (WRC) 2018 a using a bimanual robot. Index Terms-Assembly, grippers, grasping, in-hand manipulation.
2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM), 2018
We present bilateral teleoperation system for task learning and robot motion generation. Our syst... more We present bilateral teleoperation system for task learning and robot motion generation. Our system includes a bilateral teleoperation platform and a deep learning software. The deep learning software refers to human demonstration using the bilateral teleoperation platform to collect visual images and robotic encoder values. It leverages the datasets of images and robotic encoder information to learn about the intermodal correspondence between visual images and robot motion. In detail, the deep learning software uses a combination of Deep Convolutional Auto-Encoders (DCAE) over image regions, and Recurrent Neural Network with Long Short-Term Memory units (LSTM-RNN) over robot motor angles, to learn motion taught be human teleoperation. The learnt models are used to predict new motion trajectories for similar tasks. Experimental results show that our system has the adaptivity to generate motion for similar scooping tasks. Detailed analysis is performed based on failure cases of the experimental results. Some insights about the cans and cannots of the system are summarized.
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
Planning dual-arm assembly of more than three objects is a challenging Task and Motion Planning (... more Planning dual-arm assembly of more than three objects is a challenging Task and Motion Planning (TAMP) problem. The assembly planner shall consider not only the pose constraints of objects and robots, but also the gravitational constraints that may break the finished part. This paper proposes a planner to plan the dual-arm assembly of more than three objects. It automatically generates the grasp configurations and assembly poses, and simultaneously searches and backtracks the grasp space and assembly space to accelerate the motion planning of robot arms. Meanwhile, the proposed method considers gravitational constraints during robot motion planning to avoid breaking the finished part. In the experiments and analysis section, the time cost of each process and the influence of different parameters used in the proposed planner are compared and analyzed. The optimal values are used to perform real-world executions of various robotic assembly tasks. The planner is proved to be robust and efficient through the experiments.
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Papers by Kensuke Harada