#Sim-to-Real (117)

Genesis AI's open simulation platform for physical AI: one Pythonic API over a multi-physics engine (rigid, FEM, MPM, PBD, SPH, Stable Fluid, IPC), the Nyx path tracer, and Quadrants, a cross-platform Python-to-GPU compiler. It is built to be the evaluation engine for robot foundation models rather than a data factory: 30,000 parallel environments at 43M FPS, and under a zero-shot real-to-sim protocol, sim-vs-real evaluation agreement of Pearson 0.8996 with mean maximum rank violation 0.0166. Apache 2.0, 29.9k stars.

SimulationPhysics Engine

Python30k2.9k224

NVlabs/ASPIREApache-2.0

ASPIRE: Agentic /Skills Discovery for Robotics

ASPIRE is the continual-learning system NVIDIA open-sourced with the University of Michigan, UIUC, Berkeley and CMU (paper arXiv:2607.00272, Apache-2.0). No gradient descent: a coding agent repeatedly writes, runs and repairs robot control programs (code-as-policy) inside a closed-loop execution engine, promotes only verified fixes into a reusable skill library, and uses evolutionary search to escape single-trajectory patching. Driven by Claude Opus 5.5 it averages 99% success across all 80 LIBERO-Pro tasks and 100% on representative Robosuite and BEHAVIOR-1K tasks; perturbed manipulation improves by up to 77%, and unseen long-horizon tasks reach 31% zero-shot against a 4% baseline. On hardware, skills discovered in simulation transfer across embodiments to a real dual-arm station, and online learning even teaches the robot to solve a Rubik's Cube.

Code-as-PolicySkill Library

Python884784

mekion/the-bimo-projectApache-2.0

Bimo: a 45 cm Open-Source Hip-Head Biped Trained in Isaac Lab and Distilled onto an RP2040

Bimo is a 45 cm, roughly 1.6 kg hip-head biped kit: eight STS-3215 bus servos, a BNO08x 9-DoF IMU, four VL53L0X rangefinders, two 180-degree cameras, and a custom RP2040 board closing a 20 Hz control loop. The repository publishes the Python control API (1023 lines), three MCU firmware builds (1349 lines), a ROS2 wrapper (1431 lines) and an Isaac Lab training environment (874 lines: six reward terms, dense domain randomization, and a system-identified STS3215Actuator with 3.113 Hz bandwidth, directional gear backlash and a 5 ms bus delay), plus a 26MB Bimo.usd model. The CPG gait's 104 Fourier coefficients and per-joint amplitude gains are hardcoded in both Python and C, and a [256,128,64] PPO teacher distills into a [64,32] student that goes through onnx2c into the firmware, so the robot walks untethered on the MCU. All code is Apache-2.0, but the CAD and electronics the README promises are still marked coming soon: no STL, STEP, schematic, gerbers or BOM exist in the working tree or in git history, and neither release ships pretrained weights (zero assets) or a DIY assembly manual. Currently v1.1.0 with 198 stars, in pre-order status.

Bipedal robotsHumanoid

Python19887

Real2Sem2Real: 3D Semantic Augmentations for Robot Learning from a Single Human RGB Demonstration

Human video scales easily and does not need robot hardware, but existing methods require depth sensing, overlay the robot onto 2D video, or generate data only for the demonstrated objects. We present Real2Sem2Real (R2Sem2R), a real-to-semantic-to-real pipeline that turns one monocular RGB video of a human demonstration into thousands of robot demonstrations across the semantic object instances of a category. R2Sem2R tracks the SE(3) pose of each object with DINOv3-distilled Gaussian splats, outperforming existing methods and FoundationPose with estimated metric depth. R2Sem2R then aligns digital cousins, each generated from one or a few photographs, to the demonstrated objects with a semantic alignment. This alignment matches whole objects and the functional regions of target objects, such as a coffee machine's tray. R2Sem2R maps the human hand to a feasible robot grasp that extends to the cousins. Robot demonstrations are generated either in IsaacSim, for photorealistic rendering, or MuJoCo, for contact physics, and R2Sem2R can generate over 2,000 episodes in about two hours. On two real robots and four tasks, we train a vision-language-action policy on data from one human video per task, and policies trained on our data match or exceed policies trained on teleoperation across diverse object instances. The project page is available at https://real2sem2real.github.io/.

Matthew Strong, Ankush K. Dhawan, Julien Poffet · 46 views · Oct 20, 2026

Robot LearningHuman demonstrations

Oct 20, 2026

0:58

Robot Simulation Needs Accurate Physics, Not Just Visuals

@PranjalAggarw16 · 62 views · 2026-11-02

Robotics SimulationPhysics

1:00

PRISM Expands Four Humanoid Demos Into 256 Worlds

@ZappyZappy7 · 91 views · 2026-10-31

HumanoidCounterfactual Data

Learning Multi-Phase Robot Loco-Manipulation via Phase-Conditioned Policy Optimization

Complex whole-body loco-manipulation requires executing diverse maneuvers, such as crouching, grasping, and locomotion, within a single long-horizon task. While performing simple pick-and-place behaviors is well-established, scaling to complex, long-horizon tasks remains difficult. Currently, this challenge is addressed by relying on human motion data and correspondingly humanoid platforms, which limits platform independence and introduces substantial data processing overhead. Instead, we extend proximal policy optimization (PPO) to train a single policy for multi-phase tasks across diverse robotic hardware, leveraging prior task structure to shape behavior. Our method adapts PPO using a phase-conditioned critic, phase-gated reward functions, and a multi-phase reset curriculum to guide exploration. To support varied hardware, we employ a decoupled hierarchical control architecture that separates lower-level balancing dynamics from high-level phase execution. We demonstrate multi-stage loco-manipulation skills on hardware on both the Unitree G1 and a Boston Dynamics Spot equipped with a manipulator arm, and validate our design choices through ablations. Our approach enables the acquisition of complex loco-manipulation behaviors purely through environment interaction while directly incorporating high-level task structure into policy learning.

Simone Manni, Annika Wollschläger, Dario Bellicoso · 250 views · Oct 26, 2026

Whole-Body Loco-ManipulationPhase-Conditioned PPO

Oct 26, 2026

0:32

WorldGen: One Image to an Editable 3D Scene

@DeemosTech · 96 views · 2026-10-27

WorldGenAgentic Mode

MorphMimic: Morphology-Conditioned World Model and Policy Sharing Across Humanoids

MorphMimic conditions a shared differentiable world model and tracking policy on a 125-value morphology descriptor, enabling three humanoids to learn from 128,786 common motion clips and share one controller.

Lizhi Yang, Fengze Xie, Yuyang Wang · 356 views · Oct 25, 2026

HumanoidCross-Embodiment

Oct 25, 2026

ASPIRE: Agentic /Skills Discovery for Robotics

ASPIRE diagnoses robot-program failures from multimodal traces, repairs executable policies through evolutionary search, and compounds verified fixes into a reusable skill library that transfers across tasks, embodiments, and real robots.

Runyu Lu, Yubo Wu, Ethan Kou · 357 views · Jun 30, 2026

ASPIREAgentic Robotics

Jun 30, 2026

Contact-UAN: Unsupervised Actuator Networks as a Residual World Model for Humanoid Control

Humanoid policies trained in simulation can transfer qualitatively to hardware while failing to transfer the force interactions learned during training, compromising torque efficiency, load regulation, and robustness to impacts. This failure can arise from actuator sim-to-real gaps because forces depend on actuator torques. We introduce Contact-UAN, a residual world model that preserves a simulator's physics priors while correcting actuator dynamics to align simulated rollouts with recorded hardware rollouts. For humanoid motion with contact, this requires contact-consistent replay: if hardware playback in simulation begins with misaligned contact wrenches, the residual model learns to correct reset artifacts rather than actuator discrepancies. Using only onboard proprioception, Contact-UAN infers the simulator-side stance-foot pose needed to initialize replay with consistent contact forces. We show that a UAN trained on 6.5 minutes of real-world walking data generalizes to tasks that benefit from accurate force and impulse regulation and produces policies that transfer seamlessly to the real world, yielding 20% lower running torque, 43% lower force-tracking error, extreme compliance, accurate impedance control, and dynamic, robust jumps.

Nolan Fey, Khai Nguyen, Yash Prabhu · 225 views · Oct 13, 2026

HumanoidActuator Modeling

Oct 13, 2026

Compliant GMT: Making General Motion Tracking Compliant via Residual Reference Adaptation

We propose Compliant GMT (C-GMT), a modular framework for full-body compliant responses in general motion tracking. As humanoid robots move into real world deployment, they not only need to perform any motion but also respond safely and robustly to interaction forces. Existing general motion tracking policies can imitate diverse motions but are trained to be stiff, leading to unexpected and unsafe behaviors under disturbance. Prior work in compliant humanoid control trades off compliance and generality. There exist examples of full-body compliant responses, but these are heavily curated to single-reference policies. On the other hand, general motion trackers (GMT) are trained to rigidly imitate massive motion capture datasets. Our key insight is to leverage the generality of pretrained GMTs for motion tracking while introducing compliance from a residual adapter that conditions on a history of proprioception. This enables our approach, C-GMT, to achieve both general motion tracking and whole-body compliant responses. Compared to state-of-the-art general motion trackers and single-motion compliant policies, C-GMT demonstrates competitive performance in both motion tracking accuracy and compliance adherence. We further demonstrate improved safety, robustness, and error tolerance in simulation and real-world G1 experiments.

Venki Parsuram, Michelle Wang, Jin-Hyuk Hong · 259 views · Oct 15, 2026

HumanoidMotion Tracking

Oct 15, 2026

1:13

A Humanoid That Chooses Contact Points to Cross Terrain

@ZappyZappy7 · 150 views · 2026-10-25

HumanoidLocomotion

From Video to Vision: Learning Generalizable Humanoid Carrying from Human Motion

Most video-driven humanoid imitation policies still need an upper-level module to generate reference trajectories at deployment, and the resulting controllers often depend on global position in a motion-capture studio. MOVER replaces that dependency with a three-stage teacher-assistant-student pipeline. It first recovers human motion from 100 videos with GENMO, retargets it to a 29-DoF Tiangong 3.0 humanoid through GMR, and augments object geometry, scale, and pose to obtain 4,000 reference trajectories. A teacher policy learns strict motion and object tracking; an assistant relaxes global body-position tracking to improve placement generalization; and a DAgger-PPO student removes proprioceptive position and reference-joint-position inputs. At deployment, a head-mounted RGB-D camera and an object tag provide relative object pose, while QuatNet predicts the reference anchor orientation from object pose, joint state, and target location. The paper evaluates four object placements and two shape-size configurations in simulation, then demonstrates carrying on the physical robot without motion capture. Remaining limitations include limited demonstration diversity, imperfect target localization without global proprioception, and a small real-world trial count.

Ziluo Ding · 251 views · Oct 10, 2026

HumanoidObject Carrying

Oct 10, 2026

0:45

Phone video to robot digital twin in under an hour

@realfxw · 131 views · 2026-10-24

RoboSimStudioDigital twins

1:00

MorphMimic: One Morphology-Conditioned World Model and Policy Across Three Humanoids

@lzyang2000 · 151 views · 2026-10-24

World ModelsHumanoid

3D Point World Models: Point Completion Enables More Accurate Dynamics Learning

Learning predictive models of the world enables robotic control through planning and can allow robots to improvise on new tasks. Video-based dynamics models lack explicit 3D structure and accumulate geometric inconsistencies, while existing partial-point-cloud world models remain sensitive to occlusion and rollout drift. 3DPWM first completes segmented object geometry from a single calibrated RGB-D view and robot joint angles, then learns action-conditioned per-point dynamics over the completed scene. Experiments across seven datasets and two embodiments show accurate 100-300 step rollouts, stronger reward correlation, improved open-loop and closed-loop planning, adaptation to an unseen long-horizon task, and sim-to-real transfer.

Skand Peri, Hung Nguyen, Chanho Kim · 190 views · Jun 30, 2026

3D World ModelPoint Cloud Completion

Jun 30, 2026

0:55

Action-Aligned Latent Planning for Versatile Humanoid Loco-Manipulation

FIBER addresses a representation problem at the heart of versatile humanoid loco-manipulation: high-level planners need a compact abstraction, but kinematic or contact targets do not encode the state-dependent control responses that make contact-rich interaction succeed, while predicting low-level actions forces an offline planner to model high-frequency closed-loop feedback. FIBER resolves this by using the executable latent space of a frozen behavioral foundation model (BFM) as the shared planning representation. Its latent policy projection (LPP) maps each independently acquired task expert into that space by identifying latent commands whose BFM-induced actions match the expert's local control responses in physical joint-target space, regularized by a semantic term that anchors each command to the BFM backward representation of the same state. Because projection only requires the expert's actions, experts may differ in observations, objectives, and training pipelines, and are discarded once projected. Closed-loop latent rollouts from all projected skills are then aggregated to train a single perceptive flow-matching planner entirely offline, conditioned on egocentric depth, proprioception, and steering tokens derived from language or waypoints; at deployment it replans in a receding-horizon loop while the frozen BFM supplies 50 Hz state feedback. The paper further contributes AH-BFM, an adaptive-horizon BFM trained with multi-gamma forward-backward learning that keeps both short-horizon tracking fidelity and long-horizon behavioral coherence in one latent space, and successor-feature-guided behavior reuse, which composes an acquired interaction such as box carrying with native whole-body behaviors like crouching or running. In IsaacSim, across 128 variations per task, FIBER reaches 42.0% mean success versus 11.6% for projected latent replay and 3.5% for kinematic planning; on a real Unitree G1 it transfers zero-shot with 6/10 box pickup, 4/10 door opening, and 2/10 object grasping, running a 5 Hz TensorRT planner and the 50 Hz BFM on a single laptop GPU.

Yutang Lin, Guanya Shi, C. Karen Liu · 269 views · Oct 17, 2026

HumanoidMobile Manipulation

Oct 17, 2026

Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching

While recent advances in humanoid locomotion have achieved stable walking on varied terrains, capturing the agility and adaptivity of highly dynamic human motions remains an open challenge. In particular, agile parkour in complex environments demands not only low-level robustness, but also human-like motion expressiveness, long-horizon skill composition, and perception-driven decision-making. In this paper, we present Perceptive Humanoid Parkour (PHP), a modular framework that enables humanoid robots to autonomously perform long-horizon, vision-based parkour across challenging obstacle courses. Our approach first leverages motion matching, formulated as nearest-neighbor search in a feature space, to compose retargeted atomic human skills into long-horizon kinematic trajectories. This framework enables the flexible composition and smooth transition of complex skill chains while preserving the elegance and fluidity of dynamic human motions. Next, we train motion-tracking reinforcement learning (RL) expert policies for these composed motions, and distill them into a single depth-based, multi-skill student policy, using a combination of DAgger and RL. Crucially, the combination of perception and skill composition enables autonomous, context-aware decision-making: using only onboard depth sensing and a discrete 2D velocity command, the robot selects and executes whether to step over, climb onto, vault or roll off obstacles of varying geometries and heights. We validate our framework with extensive real-world experiments on a Unitree G1 humanoid robot, demonstrating highly dynamic parkour skills such as climbing tall obstacles up to 1.25m (96% robot height), as well as long-horizon multi-obstacle traversal with closed-loop adaptation to real-time obstacle perturbations.

Zhen Wu, Xiaoyu Huang, Lujie Yang · 227 views · Feb 17, 2026

HumanoidParkour

Feb 17, 2026

0:36

Blind Tight-Clearance Assembly from Proprioception Alone, Zero-Shot to Real Robots

@younghyo_park · 179 views · 2026-10-19

ManipulationSim-to-Real

Blind Precise Assembly: Proprioceptive Sim-to-Real Peg-in-Hole for Diverse Parts

Every robot already carries high-resolution contact sensors: its own joints. Legged robots and humanoids exploit them to walk, run, and track whole-body motions without vision, but there the contacts are large and forceful. Is joint sensing also enough for precise, contact-rich assembly, where the robot must find a millimeter-scale opening, mesh gear teeth, or catch a thread through light contact? We study this question by training blind assembly policies that observe only a short history of joint positions, velocities, and their own actions, without visual, force, torque, or tactile sensing. The poses of both the fixture and the part are unknown to the policy, and the grasp is randomized substantially. The policy must therefore assemble the parts through contact, sensed exclusively through its own proprioception. Trained with reinforcement learning entirely in simulation over randomized parts, grasps, and contacts, such policies learn peg insertion, gear meshing, and nut threading. The peg insertion policy transfers zero-shot to the real world and inserts pegs of different shapes and sizes in 73% of trials. Controlled comparisons with matched visual policies in simulation show that blind policies generalize far better to unseen part shapes and sizes, though they can be slower. Training such policies needs thousands of different assemblies, each simulated with its clearance intact, so we also release an open-source generator that builds them procedurally from exact convex pieces for MuJoCo; it is more accurate and faster than convex decomposition or MuJoCo's signed-distance collision. Videos and interactive simulations are at https://younghyopark.me/blind-proprioceptive-assembly/.

Antonia Bronars, Younghyo Park, Kushal Kedia · 346 views · Oct 16, 2026

Peg-in-HoleSim-to-Real

Oct 16, 2026

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#Sim-to-Real (117)

Genesis World: The Unified Multi-Physics Simulation Platform for Physical AI

ASPIRE: Agentic /Skills Discovery for Robotics

Bimo: a 45 cm Open-Source Hip-Head Biped Trained in Isaac Lab and Distilled onto an RP2040

Real2Sem2Real: 3D Semantic Augmentations for Robot Learning from a Single Human RGB Demonstration

Robot Simulation Needs Accurate Physics, Not Just Visuals

PRISM Expands Four Humanoid Demos Into 256 Worlds

Learning Multi-Phase Robot Loco-Manipulation via Phase-Conditioned Policy Optimization

WorldGen: One Image to an Editable 3D Scene

MorphMimic: Morphology-Conditioned World Model and Policy Sharing Across Humanoids

ASPIRE: Agentic /Skills Discovery for Robotics

Contact-UAN: Unsupervised Actuator Networks as a Residual World Model for Humanoid Control

Compliant GMT: Making General Motion Tracking Compliant via Residual Reference Adaptation

A Humanoid That Chooses Contact Points to Cross Terrain

From Video to Vision: Learning Generalizable Humanoid Carrying from Human Motion

Phone video to robot digital twin in under an hour

MorphMimic: One Morphology-Conditioned World Model and Policy Across Three Humanoids

3D Point World Models: Point Completion Enables More Accurate Dynamics Learning

3DPWM: Completed Point Clouds for Robot Planning

ToddlerBot Makes a Capable Humanoid for Under $6,000

FIBER: Action-Aligned Latent Planning for Humanoid Loco-Manipulation

Action-Aligned Latent Planning for Versatile Humanoid Loco-Manipulation

Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching

Blind Tight-Clearance Assembly from Proprioception Alone, Zero-Shot to Real Robots

Blind Precise Assembly: Proprioceptive Sim-to-Real Peg-in-Hole for Diverse Parts