Learning manipulation skills from a single demonstration

By Péter Englert, Marc Toussaint. 2018

54 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sergey Levine
2. Chelsea Finn
3. Li Fei-Fei
4. Pieter Abbeel
5. Silvio Savarese
6. Yuke Zhu
7. Nicolas Manfred Otto Heess
8. Ruslan Salakhutdinov

In Control and Systems Engineering

1. Sergey Levine
2. Pieter Abbeel
3. Chelsea Finn
4. Yuke Zhu
5. Li Fei-Fei
6. Silvio Savarese
7. Nicolas Manfred Otto Heess
8. Ajay Mandlekar

Citations in the corpus

• Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations (2020, background): “Imitation learning has been applied to multiple domains such as playing table tennis [22], Go [37] and video games [33], and driving autonomously [4, 31] In robotics, the idea of robots learning from human demonstrations have been extensively explored [36, 17, 23, 11, 14, 3, 5].”
• Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control (2021, background): “Imitation learning (IL) has been widely studied in the context of robotic manipulation tasks [2, 4, 5, 11, 31].”
• Catch & Carry (2019, methodology): “…approaches consist of using the demonstrations to learn local models from which a policy can be derived [Coates et al. 2008] or using the demonstrations to infer the objec-tive for the policy through inverse optimal control [Ng and Russell 2000; Ho and Ermon 2016; Englert and Toussaint 2018].”
• A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, background): “This approach is typically called inverse reinforcement learning (IRL) [275], apprenticeship learning [1, 37], or inverse optimal control [269, 103, 101].”
• Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation (2024, methodology): “Prior works also focus on multi-task or few-shot imitation learning, [25, 27, 30, 34, 46, 50, 88, 102], language-conditioned imitation learning [12, 47, 82, 83], imitation from play data [21, 57, 74, 89], using human videos [16, 24, 29, 60, 69, 80, 84, 96], and using task-specific structures [49,…”
• MimicPlay: Long-Horizon Imitation Learning by Watching Human Play (2023, methodology): “Imitation Learning (IL) has enabled robots to successfully perform various manipulation tasks [1, 4, 9, 14, 15, 22, 26, 40].”