Papers
24
Total Citations
612
H-Index
10
About
Siddarth Jain is a robotics researcher whose work centers on assistive robotics, shared autonomy, and human-robot collaboration, with a particular focus on improving quality of life for individuals with motor impairments. His most influential contributions formalize the challenge of shared autonomy as a rigorous mathematical problem: his 2016 paper on human-in-the-loop optimization (166 citations) introduced a novel framework allowing end-users themselves to drive the customization of robotic assistance, while his 2019 work on probabilistic intent recognition (113 citations) advanced the field's ability to infer human goals during collaborative teleoperation. Alongside these, his recursive Bayesian approach to intent recognition (2018, 53 citations) further solidified his reputation as a leading voice in intelligent human-robot interfaces. Early work on body-machine interfaces and grasp detection demonstrates his commitment to end-to-end assistive systems. More recently, Jain has expanded into large language model-driven task planning, reinforcement learning for dynamic grasping, and imitation learning for assembly, reflecting a broader ambition to build generalizable, adaptable robotic agents. With over 500 cumulative citations, his research portfolio represents a coherent and impactful effort to make robots genuinely useful partners for humans in real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Human-in-the-Loop Optimization of Shared Autonomy in Assistive Robotics166 citations · 2016
- 2Probabilistic Human Intent Recognition for Shared Autonomy in Assistive Robotics113 citations · 2019
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- 4Grasp detection for assistive robotic manipulation57 citations · 2016
- 5Recursive Bayesian Human Intent Recognition in Shared-Control Robotics53 citations · 2018
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- 10Design of Adaptive Compliance Controllers for Safe Robotic Assembly10 citations · 2023