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Shivam Goel

Senior Robotics Researcher specializing in neurosymbolic robot learning and manipulation

Medford, MAResearcher9 years experienceSeniorRoboticsArtificial IntelligenceHigher Education
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About

Robotics software researcher who led a Boston Dynamics SPOT project on non-prehensile manipulation of heavy boxes, combining MuJoCo-based RL, ViT-based perception, and SPOT SDK control; the work is under review for ICRA 2026. Also built a ROS planning-and-learning stack on a LoCoBot using PDDL task planning, RTAB-Map SLAM, MoveIt motion planning, and RL to recover from execution failures.

Experience

ResearcherHRI Lab – Tufts University
ResearcherMulip Lab – Tufts University
ResearcherCASAS Lab – Washington State University
Research InternCenter of AI for Social Good – University of Southern California
ResearcherIRL Lab – Washington State University

Education

Tufts Universitydoctorate, Computer Science (2026)
Washington State Universitymaster, Computer Science (2017)
Uttar Pradesh Technical Universitybachelor, Information Technology (2015)

Key Strengths

  • Led end-to-end robotics research project (concept, implementation, paper, demos)
  • Reinforcement learning policy training and hyperparameter/reward tuning for manipulation
  • Simulation-to-robot workflow (PyBullet proof-of-concept to MuJoCo to real SPOT execution)
  • Integrated perception-to-action pipeline (ViT-based detection to SPOT grasping/control)
  • Strong ROS debugging workflow (RViz visualization, TF tree analysis, rostopic inspection)
  • Built ROS-based planning + learning stack (PDDL task planning, MoveIt, RTAB-Map, RL for failure recovery)
  • Coordinated dual-robot execution via Python multithreading and synchronization

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Languages

English

Skills

Robotic ManipulationLearning-Based Robotic ManipulationRobot LearningOpen-World Robot LearningOpen-World AdaptationNovelty HandlingAffordance DiscoveryNeurosymbolic AIHybrid Learning FrameworksCognitive ArchitecturesDIARC Cognitive ArchitectureTask and Motion Planning (TAMP)Symbolic ReasoningSymbolic PlanningContinual Learning