11 papers
FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning
Steven Oh, Jason Jingzhou Liu, Tony Tao +5
Contact-rich manipulation requires force sensitivity, but many robot arms lack dedicated force sensors due to their high cost. We present Neural External Torque Estimation (NEXT),…
AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents
Arman Zharmagambetov, Chuan Guo, Ivan Evtimov +3
Autonomous AI agents that can follow instructions and perform complex multi-step tasks have tremendous potential to boost human productivity. However, to perform many of these task…
Response to Promises and Pitfalls of Deep Kernel Learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov +1
This note responds to "Promises and Pitfalls of Deep Kernel Learning" (Ober et al., 2021). The marginal likelihood of a Gaussian process can be compartmentalized into a data fit te…
FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning
Jason Jingzhou Liu, Yulong Li, Kenneth Shaw +3
Many contact-rich tasks humans perform, such as box pickup or rolling dough, rely on force feedback for reliable execution. However, this force information, which is readily availa…
Local Policies Enable Zero-shot Long-horizon Manipulation
Murtaza Dalal, Min Liu, Walter Talbott +4
Sim2real for robotic manipulation is difficult due to the challenges of simulating complex contacts and generating realistic task distributions. To tackle the latter problem, we in…
Self-Regulation and Requesting Interventions
So Yeon Min, Yue Wu, Jimin Sun +4
Human intelligence involves metacognitive abilities like self-regulation, recognizing limitations, and seeking assistance only when needed. While LLM Agents excel in many domains,…