Papers
220
Total Citations
16,199
H-Index
59
About
Siddhartha S. Srinivasa is a leading roboticist whose work spans motion planning, robotic manipulation, human-robot interaction, and perception — collectively shaping how modern robots move, grasp, and collaborate with people. He is perhaps best known for developing CHOMP (Covariant Hamiltonian Optimization for Motion Planning), a landmark trajectory optimization framework that uses functional gradient techniques to generate smooth, efficient robot paths. Published in 2009 and refined in 2013, CHOMP has accumulated over 1,700 combined citations, cementing its place as a foundational algorithm in the field. Srinivasa also co-created the Yale-CMU-Berkeley (YCB) Object and Model Set, a standardized benchmark suite for robotic grasping and manipulation research that has garnered over 1,400 citations across two publications, fundamentally changing how the community evaluates manipulation systems. His work on legible robot motion — designing movements that clearly communicate a robot's intent to human collaborators — reflects his deep investment in safe, intuitive human-robot collaboration. Additional contributions include the MOPED object recognition framework, shared control formalisms, and the BIT* sampling-based planning algorithm. Together, his body of work demonstrates a remarkable ability to bridge theoretical rigor with real-world robotic impact.
Research Focus
Key Achievements
Top Papers
- 1CHOMP: Gradient optimization techniques for efficient motion planning982 citations · 2009
- 2
- 3CHOMP: Covariant Hamiltonian optimization for motion planning738 citations · 2013
- 4
- 5Legibility and predictability of robot motion515 citations · 2013
- 6Planning-based prediction for pedestrians469 citations · 2009
- 7The MOPED framework: Object recognition and pose estimation for manipulation443 citations · 2011
- 8
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- 10A policy-blending formalism for shared control373 citations · 2013