From the 1 of 16 linked papers with an AI index.
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Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning
Ioannis Dadiotis, Mayank Mittal, Nikos Tsagarakis +1
Non-prehensile pushing to move and reorient objects to a goal is a versatile loco-manipulation skill. In the real world, the object's physical properties and friction with the floo…
Learning coordinated badminton skills for legged manipulators
Yuntao Ma, Andrei Cramariuc, Farbod Farshidian +1
Coordinating the motion between lower and upper limbs and aligning limb control with perception are substantial challenges in robotics, particularly in dynamic environments. To thi…
Multi-critic Learning for Whole-body End-effector Twist Tracking
Aravind Elanjimattathil Vijayan, Andrei Cramariuc, Mattia Risiglione +2
Learning whole-body control for locomotion and arm motions in a single policy has challenges, as the two tasks have conflicting goals. For instance, efficient locomotion typically…
GraspQP: Differentiable Optimization of Force Closure for Diverse and Robust Dexterous Grasping
René Zurbrügg, Andrei Cramariuc, Marco Hutter
Dexterous robotic hands enable versatile interactions due to the flexibility and adaptability of multi-fingered designs, allowing for a wide range of task-specific grasp configurat…
Robust Ladder Climbing with a Quadrupedal Robot
Dylan Vogel, Robert Baines, Joseph Church +3
Quadruped robots are proliferating in industrial environments where they carry sensor payloads and serve as autonomous inspection platforms. Despite the advantages of legged robots…
Learning Accurate Whole-body Throwing with High-frequency Residual Policy and Pullback Tube Acceleration
Yuntao Ma, Yang Liu, Kaixian Qu +1
Throwing is a fundamental skill that enables robots to manipulate objects in ways that extend beyond the reach of their arms. We present a control framework that combines learning…