Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input

By Petar Kormushev, Sylvain Calinon, Darwin G. Caldwell. 2011

290 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Radiological and Ultrasound Technology, Reinforcement Learning, Robotics & Control

How researchers see this paper

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

In Control and Systems Engineering

1. Sergey Levine
2. Pieter Abbeel
3. Jan Peters
4. Aude Gemma Billard
5. Harish Chaandar Ravichandar
6. George Dimitri Konidaris
7. Scott Niekum
8. Oliver Kroemer

In Mechanical Engineering

1. Aude Gemma Billard
2. George Dimitri Konidaris
3. Harish Chaandar Ravichandar
4. Scott Niekum
5. Sonia Chernova
6. Stefan Schaal
7. Athanasios S. Polydoros
8. Mrinal Kalakrishnan

In Reinforcement Learning

1. Sergey Levine
2. Scott Niekum
3. Pieter Abbeel
4. Oliver Kroemer
5. George Dimitri Konidaris
6. Aude Gemma Billard
7. Chelsea Finn
8. Mrinal Kalakrishnan

In Robotics & Control

1. Sergey Levine
2. George Dimitri Konidaris
3. Peter Pastor
4. Scott Niekum
5. Sonia Chernova
6. Oliver Kroemer
7. Mrinal Kalakrishnan
8. Aude Gemma Billard

Citations in the corpus

• A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, background): “Other recent work has focused on expanding the purview of behavioral cloning by unifying imitation learning and planning via probabilistic inference [308], utilizing additional modalities such as haptic input [211], learning to recognize and recover from errors when imitating [290, 289],”
• Learning force control policies for compliant manipulation (2011, background): “This corresponds more directly to the physical quantities that need to be controlled.”
• Recent Advances in Robot Learning from Demonstration (2020, background): “A wide range of interfaces have been explored, including haptic devices (25, 26) and virtual-reality interfaces (27– 29).”
• Interactive Language: Talking to Robots in Real Time (2022, background): “in forms like teleoperation [32]–[34], kinesthetic teaching [35], or sparse human preference feedback [36].”