Feedback motion planning and learning from demonstration in physical robotic assistance: differences and synergies

By M. Lawitzky, J. Medina, Dongheui Lee, Sandra Hirche. 2012

35 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Mechanical Engineering

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Sergey Levine
2. Chelsea Finn
3. Luc Van Gool
4. Trevor Darrell
5. Dacheng Tao
6. Frederik D. Ebert
7. Jitendra Malik
8. Li Fei-Fei

In Control and Systems Engineering

1. Sergey Levine
2. Chelsea Finn
3. Frederik D. Ebert
4. Annie Xie
5. Pieter Abbeel
6. Tengyu Ma
7. Huazhe Xu
8. Suraj Nair

In Mechanical Engineering

1. Chelsea Finn
2. Sergey Levine
3. Annie Xie
4. Yuping Luo
5. Tengyu Ma
6. Frederik D. Ebert
7. Huazhe Xu

Citations in the corpus

• Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling (2019, methodology): “Imitation learning is commonly adopted as a standard approach in robotics (Pomerleau, 1989; Schaal, 1997; Argall et al., 2009; Osa et al., 2017; Ye & Alterovitz, 2017; Aleotti & Caselli, 2006; Lawitzky et al., 2012; Torabi et al., 2018; Le et al., 2017; 2018) and many other areas such as playing gam”
• Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight (2019, methodology): “Learning from demonstrations has also been used in combination with planning , where a planning cost function is inferred from data [38, 47, 1, 26] or where tool-use is learned”