JAKUB Jakub Dworakowski DWORAKOWSKI

HRI Researcher. Robot learning & interaction systems.

ABOUT

I explore the frontiers of human-robot interaction and adaptive robot learning in the wild. I build systems that perceive, act, and interact.

I am currently an HRI Researcher at Ludo Robotics.

Previously a Staff Roboticist, where I worked across the stack that lets a device understand people and act in physical space — from compact language-conditioned action models to active-perception policies trained with PPO, alongside state-estimation and motion-planning foundations.

Before that I spent three years in Special Projects on real-time perception: event-camera calibration, occupancy grids with hard latency guarantees, and lidar pipelines built on Apple Silicon. My roots are in mechatronics (Waterloo) and interactive intelligence (Georgia Tech).

Off the clock I prototype open interaction systems — most recently a voice-driven framework for the Reachy Mini, and a long thread of research on compressing and understanding neural networks.

EXPERIENCE

Ludo Robotics -- HRI Researcher

May 2026 — Present

Exploring the frontiers of human-robot interaction and adaptive robot learning in the wild.

Apple -- Staff Roboticist

Dec 2019 — May 2026

  • Built a ~100MB language-conditioned action retrieval model for human-device interaction (~70% accuracy).
  • Trained an active-perception policy in a continuous-action setting using PPO.
  • Co-formulated a framework for human-device interaction with the design team.
  • Produced a state-estimation stack for humans and objects using EKFs and discrete Bayes filters.
  • Online controller tuning via genetic algorithm; SIMD 3×3 eigen solver; RRT expansion primitive with smoothness guarantees; binary-search collision checking (7× faster).
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Apple -- Algorithms Engineer

Jun 2016 — Dec 2019

  • Event-based camera calibration with real-time application.
  • Hard real-time knot-a-not and convolutional cubic interpolation optimized for Apple Silicon.
  • Occupancy-grid container with real-time memory-access guarantees and efficient CPU projection.
  • Online lidar pipelines using Apple SIMD and Metal compute; header-only Metal/C++ compatibility library.
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PROJECTS

Multi-Modal Interaction (Reachy Mini)

Voice-driven behavior planning (Whisper + GPT-OSS), JAX trajectory generation, open-set detection fused with EKF tracking.

Dynamic SVD Compression

Heuristic rank reduction during training that retains accuracy at roughly 20% of the original size.

Singular-Value Analysis

An SVD lens on gradient descent with/without momentum under dropout — plus a gradient-perturbation layer.

Depth-Enhanced ConvNet

Recovers RGB-D benefits without a depth channel; implemented in TensorFlow with transfer learning.

RL for PID Tuning

Custom continuous policy-gradient formulations for directly tuning controller gains online.

Neural-Driven Arm Control

Real-time closed-loop control of a live human arm with vanilla Q-learning, using a Kinect and electrode pads.

SAY HELLO

Let's build something that moves.

Open to research collaborations, consulting, and conversations about robot learning and interaction.

Email → LinkedIn →