4 citations · 4 across the 6 of their papers we have counts for
6 papers
Should We Learn Contact-Rich Manipulation Policies from Sampling-Based Planners?
Huaijiang Zhu, Tong Zhao, Xinpei Ni +4
The tremendous success of behavior cloning (BC) in robotic manipulation has been largely confined to tasks where demonstrations can be effectively collected through human teleopera…
Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?
Yuki Shirai, Tong Zhao, H. J. Terry Suh +5
Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesi…
Diffusion-based learning of contact plans for agile locomotion
Victor Dhédin, Adithya Kumar Chinnakkonda Ravi, Armand Jordana +5
Legged robots have become capable of performing highly dynamic maneuvers in the past few years. However, agile locomotion in highly constrained environments such as stepping stones…
MPC with Sensor-Based Online Cost Adaptation
Avadesh Meduri, Huaijiang Zhu, Armand Jordana +1
Model predictive control is a powerful tool to generate complex motions for robots. However, it often requires solving non-convex problems online to produce rich behaviors, which i…
Efficient Object Manipulation Planning with Monte Carlo Tree Search
Huaijiang Zhu, Avadesh Meduri, Ludovic Righetti
This paper presents an efficient approach to object manipulation planning using Monte Carlo Tree Search (MCTS) to find contact sequences and an efficient ADMM-based trajectory opti…
Enabling Remote Whole-Body Control with 5G Edge Computing
Huaijiang Zhu, Manali Sharma, Kai Pfeiffer +4
Real-world applications require light-weight, energy-efficient, fully autonomous robots. Yet, increasing autonomy is oftentimes synonymous with escalating computational requirement…