Sensing and Manipulating Built-for-Human Environments

By Rodney A. Brooks, Lijin Aryananda, A. Edsinger, P. Fitzpatrick, Charles C. Kemp, Una-May O'Reilly, Eduardo Torres-Jara, Paulina Varshavskaya, Jeff Weber. Int. J. Humanoid Robotics, 2004

344 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/446ed7532d396dc41f9988d257150a7a769e8f1d

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. Ben Upcroft
2. Lei Tai
3. Peter Corke
4. Michael Milford
5. Sai P. Selvaraj
6. Stephanie Rosenthal
7. Manuela M. Veloso
8. Fangyi Zhang

In Control and Systems Engineering

1. Jürgen Leitner
2. Peter Corke
3. Michael Milford
4. Manuela M. Veloso
5. Fangyi Zhang
6. Lei Tai
7. Sergey Levine
8. Jiwen Lu

In Mechanical Engineering

1. Peter Corke
2. Michael Milford
3. Manuela M. Veloso
4. Jürgen Leitner
5. Peter Stone

Citations in the corpus

• Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control (2015, positive): “With continuous hidden Markov models (HMMs), a humanoid robot was able to learn dual-arm manipulation tasks from human demonstrations through vision [Asfour et al. , 2008].”
• Verbalization: Narration of Autonomous Robot Experience (2016, background): “While some work generates a variety of summaries to maintain human interest (e.g., [Veloso et al., 2008]), the work does not vary the length or depth of summaries as we do.”
• Learning task constraints in operational space formulation (2017, background): “Applications include defining different task controllers for multiple end-effectors [2], [3], avoiding obstacles [4], and balancing humanoid robots [5].”