Fibration trees unify projections and decompositions for multi-robot planning; Fibration-RRT generalizes quotient and discrete RRT, is probabilistically complete, and solves problems up to 96 DOF via user-defined trees.
Orbit: A unified simulation framework for interactive robot learning environments.IEEE Robotics and Automa- tion Letters, 8(6):3740–3747, 2023
24 Pith papers cite this work. Polarity classification is still indexing.
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GLENS uses diffusion models on solver iterates to generate high-quality and diverse initial guesses for multimodal non-convex optimization, leading to faster solver convergence.
OrchardBench simulates physically-grounded, breakable, fruit-bearing apple trees on a GPU-parallel engine to benchmark autonomous harvesting robots.
TactX learns a shared latent representation across three tactile sensor modalities via joint training on paired contacts, enabling zero-shot policy transfer and higher success on pick-and-place, insertion, wiping, and reorientation tasks.
A reinforcement learning framework for AI coaching, modeled as a non-cooperative game with causal skill models, shows improved human learning outcomes in a drone racing user study over baselines.
A double-soft-belt finger module adds translation, pitch, and roll to parallel grippers for improved in-hand manipulation at low cost.
HilDA pre-trains LiDAR backbones via multi-layer and global distillation from vision models plus temporal occupancy diffusion, yielding SOTA results on detection, flow, and occupancy tasks.
RGB-S projects tactile contacts onto images as force-modulated Gaussian saliency maps via kinematics and zero-initialized conditioning, raising real-world occluded dexterous manipulation success by 26.7 percentage points over implicit baselines.
ChronoForest couples anchor-chaining tree diffusion planning with an online multi-tree orchestrator to reach 99+% success on AntMaze-Stitch splits and improve giant-stitch results by up to 34.5 points over prior diffusion methods.
WorkBenchMark is a new LEGO-based benchmark for robotic assembly tasks with an Assembly-by-Disassembly baseline that outperforms vision-language-action methods across all tiers.
SSR3D-LLM improves fine-grained 3D grounding in unified 3D-LLMs by generating and scoring sequences of latent spatial reasoning steps from the query using fixed Mask3D proposals.
A diffusion-based multi-robot planner trained on few agents generalizes to larger numbers during deployment using inter-agent attention and temporal convolution.
Contact-Grounded Policy predicts coupled robot-state and tactile trajectories with a diffusion model and maps them via a learned consistency function to executable targets for compliance controllers, outperforming standard visuotactile diffusion baselines on physical and simulated dexterous tasks.
Gaussian Process regression supplies a data-driven feed-forward term whose fidelity measure is used to lower feedback gains in high-confidence regions for soft-robot tracking control.
FrozenDrive enables zero-shot text-guided generation of consistent multi-view driving scenes via a parameter-free frozen diffusion backbone with spatio-temporal attention, improving autonomous driving models on adverse conditions via data augmentation.
LEAP enables real-time proprioceptive adaptation to unseen damage in a 6DoF soft wrist using HSA actuators by combining latent damage representations with a robust ensemble method, with conditions identified for linear rather than exponential sample complexity.
A transformer-based soft actor-critic RL policy guides overlapping coalition updates in a potential game for heterogeneous AAV task allocation, yielding 39.76% lower generalized logistics cost than heuristic baselines in 32-AAV/80-task simulations.
OpenPRC provides a schema-driven framework with five modules for GPU physics simulation, experimental vision ingestion, reservoir learning, information analysis, and physics-aware optimization to enable consistent PRC evaluation from simulations and real experiments.
MOBIUS is a multi-modal bipedal robot with hybrid reinforcement learning and force control plus an MIQCP planner that enables walking, crawling, climbing, and rolling on varied terrains.
Compute and motion are tightly intertwined in MAVs, requiring cyber-physical co-design for optimal mission metrics, as shown via analytical models, simulation, end-to-end benchmarking, and the open-sourced MAVBench tool suite.
A literature review that defines silent physical-action failures in Physical AI and identifies the lack of complete runtime authorization boundaries across surveyed technical streams.
HYMN is a multi-sensor, time-synchronized indoor-outdoor positioning dataset combining five radio technologies (GNSS, UWB, BLE, WiFi, 5G) with total-station ground truth across 48 reference points in an industrial hall.
An OctoMap frontier exploration method achieves O(number of frontiers) complexity via sensor modeling and Bayesian information gain estimation, delivering up to 54% faster exploration than standard baselines.
Zero-shot sim-to-real transfer of independently trained RL policies for cart-pole swing-up and stabilization is achieved via sensitivity-guided domain randomization, linear curriculum learning, and first-order action smoothing with Simulink switching logic.
citing papers explorer
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Fibration Trees: A Unified Approach to Multi-Robot Motion Planning
Fibration trees unify projections and decompositions for multi-robot planning; Fibration-RRT generalizes quotient and discrete RRT, is probabilistically complete, and solves problems up to 96 DOF via user-defined trees.
-
GLENS: Global Search via Learning from Solver Iterates with Diffusion Models
GLENS uses diffusion models on solver iterates to generate high-quality and diverse initial guesses for multimodal non-convex optimization, leading to faster solver convergence.
-
OrchardBench: A Physically-Grounded, GPU-Parallel Apple-Orchard Simulation Benchmark for Agricultural Robotics
OrchardBench simulates physically-grounded, breakable, fruit-bearing apple trees on a GPU-parallel engine to benchmark autonomous harvesting robots.
-
TactX: Learning Shared Tactile Representations Across Diverse Sensors
TactX learns a shared latent representation across three tactile sensor modalities via joint training on paired contacts, enabling zero-shot policy transfer and higher success on pick-and-place, insertion, wiping, and reorientation tasks.
-
AI Coaching for Accelerating Human Skill Development with Reinforcement Learning
A reinforcement learning framework for AI coaching, modeled as a non-cooperative game with causal skill models, shows improved human learning outcomes in a drone racing user study over baselines.
-
Belt-Finger: An Affordable Soft Belt-Driven Gripper for Dexterous In-Hand Manipulation
A double-soft-belt finger module adds translation, pitch, and roll to parallel grippers for improved in-hand manipulation at low cost.
-
HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training
HilDA pre-trains LiDAR backbones via multi-layer and global distillation from vision models plus temporal occupancy diffusion, yielding SOTA results on detection, flow, and occupancy tasks.
-
RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation
RGB-S projects tactile contacts onto images as force-modulated Gaussian saliency maps via kinematics and zero-initialized conditioning, raising real-world occluded dexterous manipulation success by 26.7 percentage points over implicit baselines.
-
ChronoForest: Closed-Loop Multi-Tree Diffusion Planning for Efficient Bridge Search and Route Composition
ChronoForest couples anchor-chaining tree diffusion planning with an online multi-tree orchestrator to reach 99+% success on AntMaze-Stitch splits and improve giant-stitch results by up to 34.5 points over prior diffusion methods.
-
WorkBenchMark: A LEGO-Based Assembly Benchmark with an Assembly-by-Disassembly Baseline for the Smart Manufacturing League
WorkBenchMark is a new LEGO-based benchmark for robotic assembly tasks with an Assembly-by-Disassembly baseline that outperforms vision-language-action methods across all tiers.
-
SSR3D-LLM: Structured Spatial Reasoning via Latent Steps for Fine-Grained Grounding in Unified 3D-LLMs
SSR3D-LLM improves fine-grained 3D grounding in unified 3D-LLMs by generating and scoring sequences of latent spatial reasoning steps from the query using fixed Mask3D proposals.
-
Train-Small Deploy-Large: Leveraging Diffusion-Based Multi-Robot Planning
A diffusion-based multi-robot planner trained on few agents generalizes to larger numbers during deployment using inter-agent attention and temporal convolution.
-
Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding
Contact-Grounded Policy predicts coupled robot-state and tactile trajectories with a diffusion model and maps them via a learned consistency function to executable targets for compliance controllers, outperforming standard visuotactile diffusion baselines on physical and simulated dexterous tasks.
-
Keep soft robots soft -- a data-driven based trade-off between feed-forward and feedback control
Gaussian Process regression supplies a data-driven feed-forward term whose fidelity measure is used to lower feedback gains in high-confidence regions for soft-robot tracking control.
-
FrozenDrive: Zero-Shot Text-Guided Driving Scene Generation and Data Augmentation with Parameter-Free Frozen Diffusion Model
FrozenDrive enables zero-shot text-guided generation of consistent multi-view driving scenes via a parameter-free frozen diffusion backbone with spatio-temporal attention, improving autonomous driving models on adverse conditions via data augmentation.
-
Damage Adaptation in Seconds for Architected Materials
LEAP enables real-time proprioceptive adaptation to unseen damage in a 6DoF soft wrist using HSA actuators by combining latent damage representations with a robust ensemble method, with conditions identified for linear rather than exponential sample complexity.
-
Heterogeneous AAV Logistics Task Allocation: A Reinforcement Learning Enhanced Overlapping Coalition Formation Game Approach
A transformer-based soft actor-critic RL policy guides overlapping coalition updates in a potential game for heterogeneous AAV task allocation, yielding 39.76% lower generalized logistics cost than heuristic baselines in 32-AAV/80-task simulations.
-
OpenPRC: A Unified Open-Source Framework for Physics-to-Task Evaluation in Physical Reservoir Computing
OpenPRC provides a schema-driven framework with five modules for GPU physics simulation, experimental vision ingestion, reservoir learning, information analysis, and physics-aware optimization to enable consistent PRC evaluation from simulations and real experiments.
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MOBIUS: A Multi-Modal Bipedal Robot that can Walk, Crawl, Climb, and Roll
MOBIUS is a multi-modal bipedal robot with hybrid reinforcement learning and force control plus an MIQCP planner that enables walking, crawling, climbing, and rolling on varied terrains.
-
The Role of Compute in Autonomous Aerial Vehicles
Compute and motion are tightly intertwined in MAVs, requiring cyber-physical co-design for optimal mission metrics, as shown via analytical models, simulation, end-to-end benchmarking, and the open-sourced MAVBench tool suite.
-
Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems
A literature review that defines silent physical-action failures in Physical AI and identifies the lack of complete runtime authorization boundaries across surveyed technical streams.
-
Bimanual Robot Manipulation via Multi-Agent In-Context Learning
HYMN is a multi-sensor, time-synchronized indoor-outdoor positioning dataset combining five radio technologies (GNSS, UWB, BLE, WiFi, 5G) with total-station ground truth across 48 reference points in an industrial hall.
-
Asymptotically-Bounded 3D Frontier Exploration enhanced with Bayesian Information Gain
An OctoMap frontier exploration method achieves O(number of frontiers) complexity via sensor modeling and Bayesian information gain estimation, delivering up to 54% faster exploration than standard baselines.
-
Zero-shot Transfer of Reinforcement Learning Control Policies for the Swing-Up and Stabilization of a Cart-Pole System
Zero-shot sim-to-real transfer of independently trained RL policies for cart-pole swing-up and stabilization is achieved via sensitivity-guided domain randomization, linear curriculum learning, and first-order action smoothing with Simulink switching logic.