Simulating a Dextrous Hand For Robotics With OpenUSD

Teacher-Student Dexterous Grasping System for Cluttered Scenes with Sim-to-Real Transfer

Practical Integration of Open-Source Control Architectures on Custom Quadruped Robots: Simulation Validation and Hardware Interfacing

Custom-built quadrupedal platforms offer an accessible path for robotics research, yet researchers frequently encounter a “framework gap” when porting complex open-source control software to custom hardware. This paper documents the integration and validation process of Scotty, a custom quadrupedal robot, evaluating two distinct open-source control frameworks: the torque-based MIT Mini Cheetah and the position-based CHAMP architectures. Due to minimal documentation, hardware-dependent complexities, and a tightly coupled architecture, it was difficult to achieve a satisfactory result using the MIT framework within our system’s scope. Conversely, CHAMP’s structured integration documentation enabled the deployment of a locomotion pipeline validated in Gazebo simulation alongside a functional hardware interface middle layer. To overcome CHAMP’s lack of native operational state management, we developed a custom state-based controller with a web-based GUI that safely orchestrates transitions across Idle, Ready, Down, Stand, and Walk configurations. While walking was successfully validated in the simulation environment to verify the control software pipeline, physical hardware evaluation was restricted to individual joint control, localized leg movements, and GUI-based parameter tuning. Full closed-loop hardware locomotion was not achieved, as the extensive tuning of gait parameters and controller gains under full system weight was bounded by project constraints. The integration logs, practical hurdles, and architectural lessons documented in this work are shared openly to provide a clear, transferable roadmap for future developers of robotic systems.

DemoBridge: A Simulation-in-the-Loop Toolkit for Single-View Human Demonstration Retargeting

We present DemoBridge, an toolkit that turns a single-view RGB stereo recording of a human hand demonstration into an executable, physics-validated robot-arm trajectory. Retargeting across the embodiment gap is hard. A robot arm reaches a target with a long, articulated body whose links carry far more collision volume than a hand. Solving inverse kinematics for the mapped end-effector pose often yields no collision-free solution, and a trajectory imposes this at every waypoint. A single view adds noise, leaving the demonstrated reference inaccurate. At the core of DemoBridge is a single collision-aware planner. It optimizes the whole joint trajectory at once, reasoning jointly over alternative grasp poses, whole-arm and grasped-object collision, and fidelity to the demonstrated path. A physics simulator runs in the loop. It validates each phase as it is produced and backtracks on failure, so a demonstration that cannot be reproduced as given is re-planned rather than discarded. The resulting action sequence is dynamically stable and faithful to the demonstrated manipulation. It also doubles as a ready-to-use simulation rollout for policy learning. Grasp timing is inferred automatically, and the perception backends, robot, and pipeline stages are swappable from configuration. We evaluate whole-pipeline retargeting on three real-demonstration tasks and the planner on a controlled synthetic benchmark. Our code is available at https://gitlab.kuleuven.be/u0123974/demo-bridge/ .

Aero Hand Open: A Simulation-Ready Tendon-Driven Hand for Dexterous Manipulation Learning

A tendon-driven anthropomorphic hand that is released simulation-ready, and a reinforcement learning package that lets a policy be trained entirely in simulation and run on the hand with no fine-tuning and no state estimation is presented.

DexMani: Human-Derived Manipulability Guidance for Dexterous Rotation

This work introduces DexMani, a framework that transfers human demonstrations as contact-conditioned manipulability evolution and uses it to guide downstream reinforcement learning, enabling rotation skills to be acquired across robot embodiments with distinct kinematics and active-contact configurations.

Ace! Motion Planning of Professional-Level Table Tennis Serves with a Robot Arm

Table tennis, a dynamic, compact, and popular sport, has received significant attention as a robotics benchmark over the last decades. Most of the research has focused on the rally aspect - returning an incoming ball - requiring high-speed vision, agile motion planning, and tight closed-loop control. However, the other component of table tennis gameplay - the serve - is comparatively a quite unexplored research problem, that in fact requires pushing physics modeling and control to the extremes. Achieving competitive serves with a robot presents domain-specific challenges, such as high-spin generation from a spinless ball, precise aiming, or multi-objective optimization. In this work, we present a novel approach for generating official rule-compliant serves by combining motion primitives, Model Predictive Control, and Bayesian Optimization. Serves generated in this way offer a wide and controllable variation of spins of up to 550 rad/s, and speeds of up to 6.7 m/s, matching and even surpassing those of elite table tennis players.