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cs.RO2026

ManiSkillFormer: Demonstration-Free Compositional Manipulation via Geometric Contracts and Agentic Skill Graph

Peiqi Yu, Mosam Dabhi, Shangtao Li +3

Adapting robotic manipulation to new objects and tasks often requires additional demonstrations or manual engineering. Reusable manipulation skills can reduce this effort, but adap…

cs.RO2026

Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models

Lei Zheng, Peiqi Yu, Zengqi Peng +2

Diffusion models excel at generating diverse and multimodal trajectories for robotic planning, yet their iterative denoising process introduces latency that is incompatible with hi…

cs.RO2026

Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories

Yiyuan Pan, Xusheng Luo, Hanjiang Hu +2

Scaling robot learning to long-horizon tasks remains a formidable challenge. While end-to-end policies often lack the structural priors needed for effective long-term reasoning, tr…

cs.RO2026

Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations

Peiqi Yu, Philip Huang, Chaitanya Chawla +3

Robotic assembly systems traditionally require substantial manual engineering effort to integrate new tasks, adapt to new environments, and improve performance over time. This pape…

eess.SY2026

Contingency Planning for Safety-Critical Autonomous Vehicles: A Review and Perspectives

Lei Zheng, Luyao Zhang, Peiqi Yu +4

Contingency planning is the architectural capability that enables autonomous vehicles (AVs) to anticipate and mitigate discrete, high-impact hazards, such as sensor outages and adv…