Dobb·E: An open-source, general framework for learning household robotic manipulation
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Updated
Oct 15, 2024 - G-code
Dobb·E: An open-source, general framework for learning household robotic manipulation
Official code repository of "VideoCAD: A Dataset and Model for Learning Long‑Horizon 3D CAD UI Interactions from Video" @ NeurIPS 2025
RL based agent for browser-based multiplayer battle royale game «surviv.io»
This repository contains the code for the CVPR 2020 paper "Exploring Data Aggregation in Policy Learning for Vision-based Urban Autonomous Driving"
MinBC - Minimal Behavior Cloning
Machine learning robotics engineer preparation material.
stable-baselines with JAX & Haiku
NitroGen Server is a specialized inference server for the NitroGen foundation model (originally by MineDojo). It provides a high-performance backend for generalist gaming agents, allowing them to play games by processing visual input and generating controller commands.
VQ-BeT, DiTFlow, and ARBeT policies for Push-T manipulation (LeRobot)
End-to-end self-driving AI in Forza using PyTorch, screen capture, telemetry, Grad-CAM, and virtual controller feedback.
Recurrent DDPG with Behavior Cloning for automated futures trading on FIMTX (Mini Taiwan Stock Index Futures)
Behavior Cloning pipeline for robot manipulation using Robosuite + PyTorch, MLP policy, 80% success rate
Visual behavior cloning policy for skill-chaining.
Self-driving Car Nano-degree. Behavior cloning. Driving simulation.
Robot Learning Methods
Use traditional and convolutional neural networks to the clone driving behavior and then drive a race car in a simulator
Implemented Behavior Cloning, DAgger, Double Q-Learning, Dueling DQN, and Proximal Policy Optimization (PPO) in a simulated environment and analyzed/compared their performance in terms of efficiency, stability, and generalization.
PPO deep-RL agent for the MicroRTS real-time strategy game — Master's thesis (UCLouvain), targeting and surpassing the competition winner RAISocketAI.
Soft-DTW divergence loss head for LeRobot imitation learning (temporally-elastic, phase-tolerant, pad-aware, drop-in for ACT and flow-matching). Pure PyTorch, CPU-trainable.
Preference optimization for context-aware toxicity detection using Reddit community feedback.
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