Robotics Engineer · Embodied AI · Brooklyn, NY

PRABAL KHARE

I build robot systems and study what happens when learned policies fail. Sensors, comms, localization, navigation, controls, operator tooling, and now, at NYU, whether a policy can recover when it breaks.

Perception

It sees before it moves.

A 2D LiDAR scan at the front of the chassis. Reflective-marker clustering isolates two- and four-legged shelf signatures out of the return.

Localization

PGV over RS-485.

Floor tags stream into a ROS 2 localization source backed by a tag map, with wheel-odometry fallback and safety conditions when tag confidence drops.

Navigation

Nav2 path following with zone management and sector safety gating. 150 point-to-point runs on a live factory floor.

Docking

Center check. Reposition. Or abort.

The pose is validated before the lift moves. If it fails, it retries, and if it fails again, it aborts safely instead of guessing.

SMR300

300 kg. Built to be reused.

I replaced the company's ROS 1 architecture with a ROS 2 Humble stack designed for a fleet, not a prototype, CANopen and CiA 402 drives, ros2_control, an AI camera, and an operator platform for engineers who don't write code.

Project MIRA · 2nd place, TAC Challenge Norway 2024

It had to survive the pressure hull.

An autonomous underwater vehicle where every electrical decision was final the moment the hull was sealed.

• Electrical architecture and power distribution inside a sealed AUV hull
• Hydrophone and sensor-control subsystems, PCB prototyping
• ROS / MAVROS / MAVLink control software

Autonomous VTOL UAV · Top 30 of 500, SIH 2023

Find the person from the air.

A vertical-takeoff search-and-rescue platform that flew its own scan pattern and picked victims out of thermal.

• Raspberry Pi + Pixhawk + MAVROS airframe with OpenCV and thermal imaging
• TensorFlow Lite and YOLOv5 inference on board
• Autonomous scan patterns and a custom battery management system

Kurat · Autonomous conversational robot

Three brains, so one can fail.

Perception, cognition and action are separated on purpose, each can degrade or recover without taking the others down.

• RealSense D435i + Jetson Orin Nano
• YOLOv8 perception, Whisper speech, ORB-SLAM3 localization, Nav2 navigation
• Real-time interaction under a modular autonomy split

RobotDrawing · Simulated ABB IRB140

I taught it to route. It lost.

Robotic drawing reframed as a stroke-level travelling salesman problem, solved by a learned router, which then failed to beat the classical solver it was meant to replace.

• GNN + Pointer Network, imitation learning then reinforcement learning
• Simulated annealing: 26.8 pen-ups at 195 ms. The learned router: 28.8 at 10.9 s
• Slower and worse, across 28 images. Measuring it properly is how I know

OpenDroneKit · Drone Entry

Find the crack. Then predict it.

Structural inspection that doesn't stop at detection, the crack gets projected onto the reconstruction and simulated forward in time.

• ~40,000 defect images through YOLOv8 detection and U-Net segmentation
• Cracks mapped onto COLMAP 3D reconstructions
• FEniCSx simulation estimates propagation and flags severity

Continuum · Open source

Git, but for an agent's memory.

Switch from Claude Code to Codex to Gemini without re-explaining the codebase, the bug, or what you already tried.

• Append-only SQLite event log → checkpoints → materialized current.md
• log / diff / blame / restore / branch / merge, plus semantic search
• Local-first, nothing leaves .continuum/ by default

Ripple · Active research

It stopped. Now what?

A site engineer that watches a Nav2 robot fail, recovers inside a budget you set, and asks a person when the evidence says the problem is physical, then remembers what worked at that spot.

• The model only emits tool calls; the edge validates, journals, then acts
• A keepout counts once the mask and costmap show it. An arrival counts once odometry settles
• 123 unit tests, recovery 13/13 in simulation, and never yet on physical hardware

SO101 · SmolVLA · Active research

Rule out the wiring first.

Most reported manipulation failures are not policy failures. They are camera ordering, normalisation or action scaling wearing a policy's name, so the first result here is that replayed actions reproduce their demonstrations.

• Leader and follower on the LeRobot SO101, two cameras, MuJoCo evaluation
• Recorded actions replayed through the evaluator reproduce the demonstrations
• No performance claim yet. That is the honest state of it