Addressing Ambiguity in Imitation Learning through Product of Experts based Negative Feedback
On the map: Offline RL and imitation learning from data → Imitation learning methods
Abstract (arXiv)
Programming robots to perform complex tasks is often difficult and time consuming, requiring expert knowledge and skills in robot software and sometimes hardware. Imitation learning is a method for training robots to perform tasks by leveraging human expertise through demonstrations. Typically, the assumption is that those demonstrations are performed by a single, highly competent expert. However, in many real-world applications that use user demonstrations for tasks or incorporate both user data and pretrained data, such as home robotics including assistive robots, this is unlikely to be the case. This paper presents research towards a system which can leverage suboptimal demonstrations to solve ambiguous tasks; and particularly learn from its own failures. This is a negative-feedback system which achieves significant improvement over purely positive imitation learning for ambiguous tasks, achieving a 90% improvement in success rate against a system that does not utilise negative feedback, compared to a 50% improvement in success rate when utilised on a real robot, as well as demonstrating higher efficacy, memory efficiency and time efficiency than a comparable negative feedback scheme. The novel scheme presented in this paper is validated through simulated and real-robot experiments.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 23 references that are in the map. First posted on arXiv 27 Mar 2026.
Builds on
9 of 23 references.
Closest ancestors · 3
Same direction, newest first
- 2018An Algorithmic Perspective on Imitation Learning16 Nov
- 2018HG-DAgger: Interactive Imitation Learning with Human Experts5 Oct
- 2018Safe end-to-end imitation learning for model predictive control27 Mar
Other references · 6
- 2020Learning Agile Robotic Locomotion Skills by Imitating Animalscited by 229
- 2014Interactive Policy Learning through Confidence-Based Autonomycited by 29
- 2021ILoSA: Interactive Learning of Stiffness and Attractorscited by 11
- 2021Ergodic imitation: Learning from what to do and what not to docited by 8
- 2024Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Typescited by 8
- 2023Do You Need a Hand? -- a Bimanual Robotic Dressing Assistance Schemecited by 7
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Close in wording, no citation either way
- 2022Imitation Learning by Estimating Expertise of Demonstrators
- 2021Learning Online from Corrective Feedback: A Meta-Algorithm for Robotics
- 2022Interactive Imitation Learning in Robotics: A Survey
- 2020Scalable Multi-Task Imitation Learning with Autonomous Improvement
- 2025When a Robot is More Capable than a Human: Learning from Constrained Demonstrators
- 2024VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections
- 2022Learning and Retrieval from Prior Data for Skill-based Imitation Learning
- 2025Using Non-Expert Data to Robustify Imitation Learning via Offline Reinforcement Learning