Abstract:

A robot that manipulates one object well may still fail on the next. Generalizing across diverse objects and tasks is hard because such objects vary widely in geometry, articulation, and interaction dynamics. Hierarchical policies offer a powerful approach: a high-level policy predicts sub-goal end-effector poses, and a low-level policy generates the actions to reach them. However, dominant approaches leave the high-level policy brittle to unseen objects and dependent on deterministic sub-goal heuristics that many tasks cannot provide. This thesis asks: How should sub-goals be defined, represented, and communicated from the high level to the low level policy so that a single hierarchical policy generalizes across diverse objects and tasks? We address this question through two complementary projects, both building on a prior hierarchical policy that grounds 3D sub-goal prediction in the observed scene. 

We first present demo-conditioned learning for adapting to out-of-distribution objects. Rather than fine-tuning, the policy is conditioned on a single demonstration provided at test time. We show that reasoning about the demonstration and the current observation jointly in 3D outperforms compressing the demonstration into a latent embedding, and that a single human hand demonstration can replace a teleoperated robot trajectory, improving real-world performance on challenging unseen objects.

 

We then present an uncertainty-aware hierarchical framework for tasks where sub-goals cannot be deterministically defined. Common heuristics, such as gripper open/close transitions or near-zero end-effector velocity, provide no signal for non-prehensile pushing, sliding, or manipulating levers and handles without a discrete grasp event. The framework derives candidate sub-goals through probabilistic changepoint segmentation, represents the high-level goal distribution as a mixture model over candidate sub-goals, and conditions the low-level policy on this distribution through goal-aware attention.

 

Finally, this thesis extends hierarchical manipulation policies to challenging settings: adapting to unseen objects, and modeling sub-goal uncertainty in trajectories that cannot be deterministically segmented.

Committee:
David Held (advisor)
Zackory Erickson (advisor)
Shubham Tulsiani

Jason Liu