Learning Generalizable Tool-use Skills through Trajectory Generation

Autonomous systems that efficiently utilize tools can assist humans in completing many common tasks such as cooking and cleaning. However, current systems fall short of matching human-level of intelligence in terms of adapting to novel tools. Prior works based on affordance often make strong assumptions about the environments and cannot scale to more complex, contact-rich tasks. In this work, we tackle this challenge and explore how agents can learn to use previously unseen tools to manipulate deformable objects. We propose to learn a generative model of the tool-use trajectories as a sequence of point clouds, which generalizes to different tool shapes. Given any novel tool, we first generate a tool-use trajectory and then optimize the sequence of tool poses to align with the generated trajectory. We train a single model for four different challenging deformable object manipulation tasks. Our model is trained with demonstration data from just a single tool for each task and is able to generalize to various novel tools, significantly outperforming baselines. Additional materials can be found on our project website: https://sites.google.com/view/toolgen.

Planning with Spatial-Temporal Abstraction from Point Clouds for Deformable Object Manipulation

Effective planning of long-horizon deformable object manipulation requires suitable abstractions at both the spatial and temporal levels. Previous methods typically either focus on short-horizon tasks or make strong assumptions that full-state information is available, which prevents their use on deformable objects. In this paper, we propose PlAnning with Spatial-Temporal Abstraction (PASTA), which incorporates both spatial abstraction (reasoning about objects and their relations to each other) and temporal abstraction (reasoning over skills instead of low-level actions). Our framework maps high-dimension 3D observations such as point clouds into a set of latent vectors and plans over skill sequences on top of the latent set representation. We show that our method can effectively perform challenging sequential deformable object manipulation tasks in the real world, which require combining multiple tool-use skills such as cutting with a knife, pushing with a pusher, and spreading the dough with a roller.

Learning Closed-loop Dough Manipulation Using a Differentiable Reset Module

Deformable object manipulation has many applications such as cooking and laundry folding in our daily lives. Manipulating elastoplastic objects such as dough is particularly challenging because dough lacks a compact state representation and requires contact-rich interactions. We consider the task of flattening a piece of dough into a specific shape from RGB-D images. While the task is seemingly intuitive for humans, there exist local optima for common approaches such as naive trajectory optimization. We propose a novel trajectory optimizer that optimizes through a differentiable "reset" module, transforming a single-stage, fixed-initialization trajectory into a multistage, multi-initialization trajectory where all stages are optimized jointly. We then train a closed-loop policy on the demonstrations generated by our trajectory optimizer. Our policy receives partial point clouds as input, allowing ease of transfer from simulation to the real world. We show that our policy can perform real-world dough manipulation, flattening a ball of dough into a target shape.

Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning

The goal of imitation learning is to mimic expert behavior from demonstrations, without access to an explicit reward signal. A popular class of approach infers the (unknown) reward function via inverse reinforcement learning (IRL) followed by maximizing this reward function via reinforcement learning (RL). The policies learned via these approaches are however very brittle in practice and deteriorate quickly even with small test-time perturbations due to compounding errors. We propose Imitation with Planning at Test-time (IMPLANT), a new meta-algorithm for imitation learning that utilizes decision-time planning to correct for compounding errors of any base imitation policy. In contrast to existing approaches, we retain both the imitation policy and the rewards model at decision-time, thereby benefiting from the learning signal of the two components. Empirically, we demonstrate that IMPLANT significantly outperforms benchmark imitation learning approaches on standard control environments and excels at zero-shot generalization when subject to challenging perturbations in test-time dynamics.