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Sim-to-Real

18 articles · Page 1 of 1
SmartApe AI
SmartApe AI
Nov 3, 2026 · Artificial Intelligence

DexHoldem: HKU's Poker-Table Benchmark Unifies Robot Dexterity and Reasoning Evaluation

Hong Kong University and Yisheng Technology introduce DexHoldem, a real-robot benchmark using Texas Hold'em to test both dexterous manipulation (1,470 demos, 14 skills, UR10e+Shadow hand) and embodied perception/reasoning (36 questions, 14 model combos), with metrics TCR and SPSR revealing gaps between action success and scene preservation.

DexHoldemEmbodied AINeurIPS 2026
1 likes · 10 min read
DexHoldem: HKU's Poker-Table Benchmark Unifies Robot Dexterity and Reasoning Evaluation
SmartApe AI
SmartApe AI
Nov 2, 2026 · Artificial Intelligence

RSI-Nav: Self-Improving Robot Navigation with Unlabeled Trajectories, +17.7% Success

Fudan University's RSI-Nav uses a single Qwen3.5-9B model to both follow language instructions and generate them from unlabeled trajectories, then verifies generated instructions by executing them in simulation, filtering successful ones to iteratively improve navigation performance, achieving 68.6% and 72.4% success on R2R-CE and RxR-CE benchmarks, a 15.7 and 17.7 point gain, and demonstrates real-world deployment on a DEEP Robotics M20 quadruped.

Data-Centric AIInstruction GenerationQwen3.5-9B
0 likes · 14 min read
RSI-Nav: Self-Improving Robot Navigation with Unlabeled Trajectories, +17.7% Success
Baidu Intelligent Cloud Tech Hub
Baidu Intelligent Cloud Tech Hub
Oct 30, 2026 · Artificial Intelligence

WAVE-0: 257M Latent World Model Nearly Doubles π₀.₅ Real-Robot Success Rate

Shanghai Jiao Tong University and Baidu introduce WAVE-0, a 257M-parameter latent world model that connects VLA with world action prediction, nearly doubling π₀.₅'s real-robot success rate from 38.3% to 73.3% without large-scale video pre-training, while achieving 98.7% on LIBERO and 91.47% on RoboTwin 2.0.

Embodied AIRobot ManipulationSim-to-Real
0 likes · 18 min read
WAVE-0: 257M Latent World Model Nearly Doubles π₀.₅ Real-Robot Success Rate
Machine Heart
Machine Heart
Oct 28, 2026 · Artificial Intelligence

RobotAgentBench: Benchmarking Open-World Robot Autonomy Beyond Preset Tasks

RobotAgentBench evaluates robot autonomy across 50 home layouts and 90 atomic tasks, comparing VLA/WAM policies and agent architectures; hybrid code-learned execution achieves highest scores, while recursive self-improvement yields steady gains over 10 rounds.

Agent ArchitectureRecursive Self-ImprovementRobotAgentBench
0 likes · 13 min read
RobotAgentBench: Benchmarking Open-World Robot Autonomy Beyond Preset Tasks
Baidu Intelligent Cloud Tech Hub
Baidu Intelligent Cloud Tech Hub
Oct 22, 2026 · Artificial Intelligence

Baidu Baige Adapts RLinf v0.4: LEGO-style Robot RL, 2.37x Throughput, Sim-to-Real

Baidu Baige immediately adapted RLinf v0.4, an open-source reinforcement learning framework for embodied intelligence, providing pre-built images for three scenarios: Cosmos3 fine-tuning on LIBERO, Pi0.5 evaluation via ApxInf, and sim-to-real collaborative training using RL-Co, while detailing RLinf v0.4's modular robot interfaces, four new real-robot algorithms, expanded model support, realistic simulation benchmarks, and domestic chip compatibility, alongside Baige's full-chain AI Infra optimizations across data pipeline, training, simulation, and inference.

AI infrastructureBaidu BaigeCoRL 2026
0 likes · 16 min read
Baidu Baige Adapts RLinf v0.4: LEGO-style Robot RL, 2.37x Throughput, Sim-to-Real
Machine Heart
Machine Heart
Sep 25, 2026 · Artificial Intelligence

ULTRA: Unified Control for Humanoid Tracking & Goal-Driven Manipulation

ULTRA unifies motion tracking and sparse goal control in a single policy for humanoid robots, combining physics-driven motion retargeting, policy distillation, and RL fine-tuning, validated on Unitree G1 with first-person point cloud perception.

Sim-to-RealUnitree G1humanoid robotics
0 likes · 9 min read
ULTRA: Unified Control for Humanoid Tracking & Goal-Driven Manipulation
21CTO
21CTO
Sep 18, 2026 · Artificial Intelligence

OpenArm Open-Sources 7-DOF Humanoid Arm: Embodied AI's 'Linux Moment'

Tokyo-based Enactic_ai fully open-sources OpenArm, a 7-DOF humanoid robotic arm including 3D-printable CAD files, BOM, motor firmware, ROS2 drivers, web-based MuJoCo simulation, Isaac Sim integration, and a leader arm for zero-latency force-feedback teleoperation, dramatically lowering the cost barrier for embodied AI research.

Embodied AIIsaac SimMuJoCo
0 likes · 5 min read
OpenArm Open-Sources 7-DOF Humanoid Arm: Embodied AI's 'Linux Moment'
Machine Heart
Machine Heart
Sep 17, 2026 · Artificial Intelligence

World Synesthesia Model Achieves Robust Dexterous In-Hand Manipulation Under Real-World Perturbations

Sharpa Robotics' World Synesthesia Model (WSM), accepted at CoRL 2026, unifies visual geometry, tactile contact, proprioception, and action history into a reusable world model state, enabling a 22-DoF five-finger hand to achieve robust, generalizable in-hand rotation across unseen objects and real-world perturbations through clean depth supervision and recurrent memory.

CoRL 2026Sim-to-RealWorld Models
0 likes · 12 min read
World Synesthesia Model Achieves Robust Dexterous In-Hand Manipulation Under Real-World Perturbations
Machine Heart
Machine Heart
Sep 15, 2026 · Artificial Intelligence

REAL: Embodied Agents Navigate Open Worlds Without Oracle Perception or Perfect Instructions

Researchers from Shanghai Jiao Tong University and Shanghai AI Lab introduce REAL, an ECCV 2026 framework that enables embodied agents to actively explore unknown environments, disambiguate vague user instructions through dialogue, and execute mobile manipulation tasks via a unified MCP tool interface, achieving 78.3% real-world success on a dual-arm robot.

Active ExplorationECCV 2026Embodied AI
0 likes · 19 min read
REAL: Embodied Agents Navigate Open Worlds Without Oracle Perception or Perfect Instructions
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Sep 13, 2026 · Artificial Intelligence

PhyFilter: Physics-Based Filtering Enables Robot Generalization Beyond Data Scaling

Researchers from Beihang University and Nanyang Technological University propose PhyFilter, a lightweight plug-and-play module that uses physical differential structures and real-time feedback to correct learning residuals, allowing robots to generalize across unseen terrains and disturbances without massive datasets.

PhyFilterSim-to-Realacceleration estimation
0 likes · 17 min read
PhyFilter: Physics-Based Filtering Enables Robot Generalization Beyond Data Scaling
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Sep 12, 2026 · Artificial Intelligence

Robots Retracing LLMs' Scaling Path: LightNav-0 & Light REACT Explained

Light Source Innovation, founded by ex-OpenAI RLHF expert Jiang Xu, releases LightNav-0 for zero-shot cross-morphology navigation and Light REACT for whole-body resilience control, applying LLM-style scalable pre-training, alignment, and deployment paradigms to embodied AI with sim-to-real synthetic data and preference-aligned RL.

Embodied AIRLHFSim-to-Real
0 likes · 14 min read
Robots Retracing LLMs' Scaling Path: LightNav-0 & Light REACT Explained
Machine Heart
Machine Heart
Sep 11, 2026 · Artificial Intelligence

PhyFilter: Physics-Based Feedback Lets Robots Generalize Without Massive Data

Researchers from Beihang University and NTU propose PhyFilter, a plug-and-play physics-informed filter that corrects neural network errors using real-time robot state feedback and known differential structures, enabling zero-shot generalization across quadrupeds, drones, aerial manipulators, and acceleration estimation with only flat-ground simulation training.

PhyFilterRobot LearningSim-to-Real
0 likes · 18 min read
PhyFilter: Physics-Based Feedback Lets Robots Generalize Without Massive Data
TonyBai
TonyBai
Sep 7, 2026 · Artificial Intelligence

Physical AI Gold Rush: Why Robotics Is the Next Trillion-Dollar Frontier

This analysis explores the paradigm shift from digital AI to physical AI, detailing how Vision-Language-Action models and sim-to-real training are revolutionizing robotics, and outlines four high-margin software business models that avoid heavy hardware investment.

Physical AIRobotics Business ModelsRobotics Middleware
0 likes · 16 min read
Physical AI Gold Rush: Why Robotics Is the Next Trillion-Dollar Frontier
Machine Heart
Machine Heart
Jul 23, 2026 · Artificial Intelligence

How a 3D Generation Startup Achieved 139.6× Faster, Fully Safe Trajectory Optimization for Robotics

The article details how Yingmu Technology’s cuNRTO paper, nominated for the RSS 2026 Outstanding Paper Award, moves nonlinear robust trajectory optimization onto GPUs, delivering up to 139.6× speedup while preserving 100% safety constraints, and situates this breakthrough within the company’s broader 3D‑to‑embodied‑AI research roadmap.

3D generationEmbodied AIGPU Acceleration
0 likes · 14 min read
How a 3D Generation Startup Achieved 139.6× Faster, Fully Safe Trajectory Optimization for Robotics
Data Party THU
Data Party THU
May 18, 2026 · Artificial Intelligence

Engineering Sim‑to‑Real Migration for Embodied Intelligent Robots

The article presents a comprehensive engineering guide for embodied intelligent robots, detailing the three core Sim‑to‑Real migration technologies—high‑fidelity simulation adaptation (Isaac Sim), dynamics parameter identification with digital‑twin synchronization, and domain‑randomized pipelines—while comparing Isaac Sim and PyBullet, offering platform‑selection advice, and providing concrete rendering‑physics trade‑off configurations with performance metrics.

Domain RandomizationEmbodied AIIsaac Sim
0 likes · 20 min read
Engineering Sim‑to‑Real Migration for Embodied Intelligent Robots
Xiaomi Tech
Xiaomi Tech
Feb 5, 2026 · Artificial Intelligence

TacRefineNet: A Tactile‑Driven Model for Millimeter‑Precision Robotic Grasp Refinement

TacRefineNet leverages high‑resolution tactile sensors, multimodal fusion of fingertip touch and proprioception, and a goal‑conditioned refinement network to achieve millimeter‑level grasp adjustments without vision or 3D models, demonstrating zero‑shot deployment and robust generalization across diverse automotive‑factory parts in both simulation and real‑world tests.

Sim-to-RealZero-Shot Learninggrasp refinement
0 likes · 8 min read
TacRefineNet: A Tactile‑Driven Model for Millimeter‑Precision Robotic Grasp Refinement