11 accepted papers
Recent imitation learning (IL) algorithms – such as flow-matching and diffusion policies – demonstrate remarkable performance in learning complex manipulation tasks. However, these policies often fail even when operating within their training distribution due to extreme sensitivity to initial condit…
A major appeal of learning from demonstrations or imitation learning (IL) in robotics is that it learns a policy directly from lay users. However, Lay users may inadvertently provide erroneous demonstrations that lead to learning of policies that are inaccurate and hence, unsafe for humans and/or ro
This paper proposes a novel autonomous dynamic system (ADS) based controller for trajectory learning from demonstration (LfD). We call our method Learning Stable Dynamics via Iterative Quadratic Programming (LSD-IQP). LSD-IQP learns an energy function and an ADS from demonstrations via semi-infinite…
In this paper, we propose a novel network architecture for visual imitation learning that exploits neural radiance fields (NeRFs) and key-point correspondence for self-supervised visual motor policy learning. The proposed network architecture incorporates a dynamic system output layer for policy lea…
Learning tasks only from raw video demonstrations is the current state of the art in robotics visual imitation learning research. The implicit assumption here is that all video demonstrations show an optimal/sub-optimal way of performing the task. What if that is not true? What if one or more videos…
We are interested in learning from demonstration (LfD) that can both learn and execute a trajectory and evaluate the quality of a previously unseen trajectory in the domain of assistive robotics. To this end, we propose a novel continuous inverse optimal control (IOC) formulation that simultaneously…
Imitation learning (IL) frameworks in robotics typically assume that a domain expert's demonstration always contains a correct way of doing the task. Despite its theoretical convenience, this assumption has limited practical values for an IL-powered robot in real world. There are many reasons for an…
This paper proposes a dynamic system based learning from demonstration approach to teach a robot activities of daily living. The approach takes inspiration from human movement literature to formulate trajectory learning as an optimal control problem. We assume a weighted combination of basis objecti…
This paper presents a framework to learn the reward function underlying high-level sequential tasks from demonstrations. The purpose of reward learning, in the context of learning from demonstration (LfD), is to generate policies that mimic the demonstrator's policies, thereby enabling imitation lea…
A major goal of learning from demonstration is task generalization via observation of a teacher. In this paper, we propose a novel framework for learning motion from a single demonstration. Our approach reconstructs the demonstrated trajectory's phase space curve via a linear piece-wise regression m…
High-level human activities often have rich temporal structures that determine the order in which atomic actions are executed. We propose the Temporal Context Graph (TCG), a temporal reasoning model that integrates probabilistic inference with Allen's interval algebra, to capture these temporal stru…