4 citations · 4 across the 10 of their papers we have counts for
13 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…
OATH: Object-Agnostic Tracking for Dexterous Hands, Enabling Rapid Physics-based Retargeting
Solvin Sigurdson, Prasanna Sriganesh, Juan Borbon +3
Retargeting human demonstrations into dynamically feasible dexterous hand trajectories requires resolving complex contact physics, yet current methods either omit physical constrai…
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…
SAGA: Open-World Mobile Manipulation via Structured Affordance Grounding
Kuan Fang, Yuxin Chen, Xinghao Zhu +3
We present SAGA, a versatile and adaptive framework for visuomotor control that can generalize across various environments, task objectives, and user specifications. To efficiently…
Generative Models From and For Sampling-Based MPC: A Bootstrapped Approach For Adaptive Contact-Rich Manipulation
Lara Brudermüller, Brandon Hung, Xinghao Zhu +4
We present a generative predictive control (GPC) framework that amortizes sampling-based Model Predictive Control (SPC) by bootstrapping it with conditional flow-matching models tr…
Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control
Albert H. Li, Brandon Hung, Aaron D. Ames +3
Recent advancements in parallel simulation and successful robotic applications are spurring a resurgence in sampling-based model predictive control. To build on this progress, howe…
Versatile Loco-Manipulation through Flexible Interlimb Coordination
Xinghao Zhu, Yuxin Chen, Lingfeng Sun +4
The ability to flexibly leverage limbs for loco-manipulation is essential for enabling autonomous robots to operate in unstructured environments. Yet, prior work on loco-manipulati…
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…
Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation
Jan Brüdigam, Ali-Adeeb Abbas, Maks Sorokin +7
Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks re…