HERB: a home exploring robotic butler
By Siddhartha S. Srinivasa, D. Ferguson, Casey Helfrich, Dmitry Berenson, Alvaro Collet, Rosen Diankov, Garratt Gallagher, Geoffrey A. Hollinger, James J. Kuffner Jr., Michael Vande Weghe. Auton. Robots, 2010
356 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/7aabc9c78ba9d66aa680daa24c6ab7d4eeeffca6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Siddhartha S. Srinivasa
- Ashutosh Saxena
- J. Bagnell
- Nathan D. Ratliff
- Manuel Martinez
- Alvaro Collet
- Matthew Zucker
- M. Dogar
In Computer Vision and Pattern Recognition
- Siddhartha S. Srinivasa
- Ashutosh Saxena
- Sergey Levine
- Luc Van Gool
- Li Fei-Fei
- Jitendra Malik
- Trevor Darrell
- Dacheng Tao
In Control and Systems Engineering
- J. Bagnell
- Sergey Levine
- Nathan D. Ratliff
- Siddhartha S. Srinivasa
- Ashutosh Saxena
- Matthew Zucker
- Jaeyong Sung
- Dipendra Kumar Misra
Citations in the corpus
- A Planning Framework for Non-Prehensile Manipulation under Clutter and Uncertainty (2012, positive): “We conducted experiments in simulation and on HERB (Srinivasa et al 2009) to evaluate the performance of our planner.”
- Efficient multi-view object recognition and full pose estimation (2010, background): “I. INTRODUCTION There has been recent renewed interest ([1–4] to name a few) in enabling mobile manipulators to perform useful tasks in unstructured human environments, like homes and offices.”
- End-to-end dexterous manipulation with deliberate interactive estimation (2012, methodology): “HERB [3] uses the generalized approach to tracking movable objects (GATMO) [4] during navigation.”
- Integrated task and motion planning in belief space (2013, background): “Advances in perception, navigation and motion planning have enabled sophisticated manipulation systems that interleave perception, planning and acting in realistic domains (e.g., those of Srinivasa et al. (2009), Rusu et al. (2009), and Pangercic et al. (2010)).”
- CHOMP: Covariant Hamiltonian optimization for motion planning (2013, background): “0 mobile manipulation platforms [63], the LittleDog quadruped [74], Carnegie Mellon’s Andy Autonomous Robotics Manipulation-Software (ARM-S) platform, and the Willow Garage PR2 robot.”
- Human preferences for robot-human hand-over configurations (2011, background): “1) developed for personal assistance tasks in home environments [18].”
- Automated Construction of Robotic Manipulation Programs (2010, methodology): “target objects [Stilman (2007)], slide objects along a surface, or compliant whole-body humanoid control [Sentis (2007)].”
- Tell me Dave: Context-sensitive grounding of natural language to manipulation instructions (2014, background): “…different manipulation and navigational skills such as grasping (Kroemer et al., 2010; Lenz et al., 2013), mixing (Bollini et al., 2012), pushing (Srinivasa et al., 2010), placing (Barry et al., 2013; Jiang et al., 2012), constructing semantic maps (Walter et al., 2013), and high degree of…”
- The MOPED framework: Object recognition and pose estimation for manipulation (2011, methodology): “In our particular application, latency is a more critical factor than throughput, as our robot HERB (Srinivasa et al. 2010) must interact with the world in real time.”
- Sampling-Based Robot Motion Planning: A Review (2014, background): “Ever since, welding multi-degree of freedom (DOF) robots [73], industrial robots [74], domestic robots [75], [76] and urban self-driving vehicles have used sampling based planning [77], [78].”