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Rushabh Dhoke

Mid-level Robotics/Mechatronics Engineer specializing in ROS 2, SLAM, and sim-to-real autonomy

Newark, DERobotics Research Assistant4 years experienceMid-LevelRoboticsAutonomous VehiclesAerospace & Defense
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About

Robotics software engineer focused on sim-to-real deployment: built an Isaac Sim/Isaac Lab PPO training pipeline with domain randomization for vision-conditioned quadruped locomotion and integrated a RealSense D435i into a ROS2 stack on hardware. Also worked on an autonomous surface vessel, standardizing ROS2 interfaces across Jetson, microcontroller, GPS/IMU and motor controllers, using structured logging/replay to debug real-time oscillations and improve path tracking.

Experience

Robotics Research AssistantDynamic Vision Lab
Robotics Research AssistantCenter of Robotics and Autonomous Systems
Systems EngineerTata Consultancy Services

Education

University of Delawaremaster, Automation, Robotics, and Mechatronics (2026)
University of Mumbaibachelor, Mechanical Engineering (2022)

Key Strengths

  • End-to-end sim-to-real robotics integration (Isaac Sim training pipeline to real quadruped deployment)
  • Strong ROS2 systems engineering (TF2 trees, launch files, QoS, device-wrapping nodes)
  • Sensor/actuator integration and calibration across GPS/IMU/motor controllers with consistent timing
  • Data-driven debugging using structured logging and replay to diagnose timing/noise issues
  • Real-time control optimization (filtering, controller retuning, rate limits) validated via iterative field tests
  • Latency-aware perception pipeline design (depth denoising + temporal stabilization for robust gesture commands)
  • Built ROS2 vision pipeline to localize KUKA LBR iiwa end-effector using ArUco markers with FK/IK and frame transforms
  • Strong calibration and time-sync rigor (hand-eye calibration validation with boot-time error thresholds; TF/timestamp drift mitigation)
  • Robust perception under noise (detection gating and rejecting high-residual PnP results to reduce pose jitter)
  • Real-time ML deployment on edge hardware (PPO policy Isaac Lab -> ONNX -> TensorRT FP16 on Jetson Orin)
  • Demonstrated latency reduction from ~70–90ms to ~12–15ms via TensorRT optimizations, multithreading, and dedicated CUDA stream
  • Hybrid estimation expertise (TorchScript LSTM fused with UKF) for underwater robot surfacing prediction
  • Production-grade reliability practices (explicit QoS, lifecycle nodes, health checks, safe fallbacks, bag-driven regression tests in CI)
  • Controls/motion safety awareness (joint-limit/singularity checks, accel/velocity/jerk limits, impedance mode during contact)

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Languages

English

Skills

RoboticsMechatronicsIsaac SimIsaac LabOmniverseUSD (Universal Scene Description)ROS 2Nav2ros2 bagGazeboMAVROSPX4PerceptionSensor fusionState estimation