Mastering Agile Tasks with Limited Trials

Embodied robots nowadays can already handle many real-world manipulation tasks. However, certain other real-world tasks (e.g., shooting a basketball into a hoop) are highly agile and require high execution precision, presenting additional challenges for methods primarily designed for quasi-static manipulation tasks. This leads to increased efforts in costly data collection, laborious reward design, or complex motion planning. Such tasks, however, are far less challenging for humans. Say a novice basketball player typically needs only $\sim$10 attempts to make their first successful shot, by roughly imitating a motion prior and then iteratively adjusting their motion based on the past outcomes. Inspired by this human learning paradigm, we propose the Adaptive Diffusion Action Plannin (ADAP) algorithm, a simple & scalable approach which iteratively refines its action plan by few real-world trials within a learned prior motion pattern, until reaching a specific goal. Experiments demonstrated that ADAP can learn and accomplish a wide range of goal-conditioned agile dynamic tasks with human-level precision and efficiency directly in real-world, such as throwing a basketball into the hoop in fewer than 10 trials. Project website:https://adap-robotics.github.io/ .

SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation

Real-world tasks such as garment manipulation and table rearrangement demand robots to perform generalizable, highly precise, and long-horizon actions. Although imitation learning has proven to be an effective approach for teaching robots new skills, large amounts of expert demonstration data are still indispensible for these complex tasks, resulting in high sample complexity and costly data collection. To address this, we propose Semantic Keypoint Imitation Learning (SKIL), a framework which automatically obtain semantic keypoints with help of vision foundation models, and forms the descriptor of semantic keypoints that enables effecient imitation learning of complex robotic tasks with significantly lower sample complexity. In real world experiments, SKIL doubles the performance of baseline methods in tasks such as picking a cup or mouse, while demonstrating exceptional robustness to variations in objects, environmental changes, and distractors. For long-horizon tasks like hanging a towel on a rack where previous methods fail completely, SKIL achieves a mean success rate of 70\% with as few as 30 demonstrations. Furthermore, SKIL naturally supports cross-embodiment learning due to its semantic keypoints abstraction, our experiments demonstrate that even human videos bring considerable improvement to the learning performance. All these results demonstrate the great success of SKIL in achieving data-efficint generalizable robotic learning. Visualizations and code are available at: https://skil-robotics.github.io/SKIL-robotics/.