4 citations · 4 across the 8 of their papers we have counts for
10 papers
PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios
Ali Zamani, Soroush Esfahanian, Erfan Shahabi +3
Understanding the social intelligence of large language models (LLMs) is increasingly important for applications involving human-AI interaction and multi-agent environments. Social…
Turning Video Models into Generalist Robot Policies
Sizhe Lester Li, Evan Kim, Xingjian Bai +4
Video generative models have emerged as a promising robotics backbone, capable of generating videos that depict the completion of complex tasks across embodiments and environments.…
PRIME: Physically-consistent Robotic Inertial and Motion Estimation for Legged and Humanoid Robots
Jiarong Kang, Kunzhao Ren, Tao Pang +1
Humanoid and legged robots interact with the environment through intermittent contacts, making accurate motion estimation fundamentally dependent on reasoning about contact dynamic…
Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation
John Z. Zhang, Maks Sorokin, Jan Brüdigam +14
This paper presents a sim-to-real approach that enables legged robots to dynamically manipulate large and heavy objects with whole-body dexterity. Our key insight is that by perfor…
Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets
Simin Liu, Tong Zhao, Bernhard Paus Graesdal +5
If we consider human manipulation, it is clear that contact-rich manipulation (CRM)-the ability to use any surface of the manipulator to make contact with objects-can be far more e…
Dexterous Contact-Rich Manipulation via the Contact Trust Region
H. J. Terry Suh, Tao Pang, Tong Zhao +1
What is a good local description of contact dynamics for contact-rich manipulation, and where can we trust this local description? While many approaches often rely on the Taylor ap…
Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization
Lujie Yang, H. J. Terry Suh, Tong Zhao +5
We present a low-cost data generation pipeline that integrates physics-based simulation, human demonstrations, and model-based planning to efficiently generate large-scale, high-qu…
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…
Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation
Xuanlin Li, Tong Zhao, Xinghao Zhu +3
Contact-rich bimanual manipulation involves precise coordination of two arms to change object states through strategically selected contacts and motions. Due to the inherent comple…
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…