Convex Controller Synthesis for Robot Contact

Phạm, Hưng; Pham, Quang-Cuong

doi:10.1109/lra.2020.2975725

Cited by 17 publications

(18 citation statements)

References 23 publications

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“…In future work, we aim at improving the force tracking based on the extension of [40] that is currently limited to translation forces (i.e. non-moments).…”

Section: Discussionmentioning

confidence: 99%

Humanoid Control Under Interchangeable Fixed and Sliding Unilateral Contacts

Samadi¹,

Roux²,

Tanguy³

et al. 2021

Preprint

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In this letter, we propose a whole-body control strategy for humanoid robots in multi-contact settings that enables switching between fixed and sliding contacts under active balance. We compute, in real-time, a safe center-of-mass position and wrench distribution of the contact points based on the Chebyshev center. Our solution is formulated as a quadratic programming problem without a priori computation of balance regions. We assess our approach with experiments highlighting switches between fixed and sliding contact modes in multi-contact configurations. A humanoid robot demonstrates such contact interchanges from fully-fixed to multi-sliding and also shuffling of the foot. The scenarios illustrate the performance of our control scheme in achieving the desired forces, CoM position attractor, and planned trajectories while actively maintaining balance.

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“…In future work, we aim at improving the force tracking based on the extension of [40] that is currently limited to translation forces (i.e. non-moments).…”

Section: Discussionmentioning

confidence: 99%

Humanoid Control Under Interchangeable Fixed and Sliding Unilateral Contacts

Samadi¹,

Roux²,

Tanguy³

et al. 2021

Preprint

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“…In tasks that have complex dynamics, where instability is a key consideration, the choice of the low-level control law in each MPs define the upper limit of the overall system performance. Hence, incorporating advanced robust control laws [19] is a promising direction.…”

Section: Discussionmentioning

confidence: 99%

Learning Sequences of Manipulation Primitives for Robotic Assembly

Vuong¹,

Pham²,

Pham³

2020

Preprint

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This paper explores the idea that skillful assembly is best represented as dynamic sequences of Manipulation Primitives, and that such sequences can be automatically discovered by Reinforcement Learning. Manipulation Primitives, such as "Move down until contact", "Slide along x while maintaining contact with the surface", have enough complexity to keep the search tree shallow, yet are generic enough to generalize across a wide range of assembly tasks. Policies are learned in simulation, and then transferred onto the physical platform. Direct sim2real transfer (without retraining in real) achieves excellent success rates on challenging assembly tasks, such as round peg insertion with 100 micron clearance or square peg insertion with large hole position/orientation estimation errors.

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“…Force Sensing and Force Control are essential to many industrial applications, from contact-based inspection to assembly, sanding, deburring, and polishing [1]- [3]. Typically, a 6-axis Force/Torque (F/T) sensor is mounted between the robot's wrist and the end-effector in order to measure the forces and torques exerted by the environment onto the robot (the external wrench).…”

Section: Introductionmentioning

confidence: 99%