10 papers
SkillFab: An Agent-Native Skill Production Platform
Anjie Xu, Yifeng Cai, Yi Li +5
SkillFab is an agent-native platform for turning missing capabilities into reviewed, reusable Agent Skills. At runtime, agents first search for reusable skills; when no adequate sk…
Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors
Zixing Wang, Kausik Sivakumar, Jinghuan Shang +5
Bridging the sim-to-real gap is a core challenge in deploying learned manipulation policies. Sim-to-real learning is attractive because it can replace expensive real robot demonstr…
ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors
Zifan Xu, Ran Gong, Maria Vittoria Minniti +10
Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data. While human demonstrations (e.g., through teleoperation) serve as…
Passive iFIR filters for data-driven velocity control in robotics
Yi Zhang, Zixing Wang, Fulvio Forni
We present a passive, data-driven velocity control method for nonlinear robotic manipulators that achieves better tracking performance than optimized PID with comparable design com…
SkillTester: Benchmarking Utility and Security of Agent Skills
Leye Wang, Zixing Wang, Anjie Xu
This technical report presents SkillTester, a tool for evaluating the utility and security of agent skills. Its evaluation framework combines paired baseline and with-skill executi…
PPGuide: Steering Diffusion Policies with Performance Predictive Guidance
Zixing Wang, Devesh K. Jha, Ahmed H. Qureshi +1
Diffusion policies have shown to be very efficient at learning complex, multi-modal behaviors for robotic manipulation. However, errors in generated action sequences can compound o…