Long-horizon tasks, usually characterized by complex subtask dependencies, present a
significant challenge in manipulation planning. Skill chaining is a practical approach to …

Imitation learning from human demonstrations can teach robots complex manipulation skills,
but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) …

We consider the problem of sequential robotic manipulation of deformable objects using
tools. Previous works have shown that differentiable physics simulators provide gradients to …

Effective planning of long-horizon deformable object manipulation requires suitable
abstractions at both the spatial and temporal levels. Previous methods typically either focus …

The ability to plan for multi-step manipulation tasks in unseen situations is crucial for future
home robots. But collecting sufficient experience data for end-to-end learning is often …

Objects rarely sit in isolation in human environments. As such, we'd like our robots to reason
about how multiple objects relate to one another and how those relations may change as the …

To assist with everyday human activities, robots must solve complex long-horizon tasks and
generalize to new settings. Recent deep reinforcement learning (RL) methods show promise …

Developing intelligent robots for complex manipulation tasks in household and factory
settings remains challenging due to long-horizon tasks, contact-rich manipulation, and the …

Robotic manipulation in cluttered environments requires synergistic planning among
prehensile and non-prehensile actions. Previous works on sampling-based Task and Motion …

To assist with everyday human activities, robots must solve complex long-horizon tasks and
generalize to new settings. Recent deep reinforcement learning (RL) methods show …