The empirical success of derivative-free methods in reinforcement learning for planning through contact seems at odds with the perceived fragility of classical gradient-based …
This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled bipedal robot to perform robust and versatile dynamic jumps in the real world. We present a …
We present a framework for bi-level trajectory optimization in which a system's dynamics are encoded as the solution to a constrained optimization problem and smooth gradients of this …
Dexterous manipulation tasks often require contact switching, where fingers make and break contact with the object. We propose a method that plans trajectories for dexterous …
A Wu, M Guo, CK Liu - arXiv preprint arXiv:2207.00195, 2022 - arxiv.org
To fully utilize the versatility of a multi-fingered dexterous robotic hand for executing diverse object grasps, one must consider the rich physical constraints introduced by hand-object …
This paper focuses on robustness to disturbance forces and uncertain payloads. We present a novel formulation to optimize the robustness of dynamic trajectories. A straightforward …
E Huang, X Cheng, Y Mao, A Gupta… - … Journal of Robotics …, 2023 - journals.sagepub.com
The central theme in robotic manipulation is that of the robot interacting with the world through physical contact. We tend to describe that physical contact using specific words that …
Y Zhu, Z Pan, K Hauser - 2021 IEEE International Conference …, 2021 - ieeexplore.ieee.org
We present a bilevel, contact-implicit trajectory optimization (TO) formulation that searches for robot trajectories with learned soft contact models. On the lower-level, contact forces are …
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However,(1) …