MuJoCo
PulseAugur coverage of MuJoCo — every cluster mentioning MuJoCo across labs, papers, and developer communities, ranked by signal.
10 day(s) with sentiment data
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New research revisits action factorization for complex RL spaces · 2 sources tracked
A new research paper explores methods for handling complex action spaces in reinforcement learning, particularly those that combine discrete and continuous actions. The study analyzes various factorization techniques ac…
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New open-source simulator MuJoFil targets high-fidelity vision RL training
A new open-source simulator called MuJoFil has been developed, aiming to address limitations in existing tools like MuJoCo for high-fidelity vision reinforcement learning (RL) training. MuJoFil combines Nvidia's GPU-nat…
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New research unifies PPO-Clip and KL-PPO algorithms
Researchers have demonstrated that the clipped surrogate gradient in Proximal Policy Optimization (PPO) can be precisely replicated by a Kullback-Leibler surrogate with a per-sample coefficient. This equivalence holds t…
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New CRAX benchmark accelerates safe reinforcement learning evaluations
Researchers have introduced CRAX, a new benchmark designed to accelerate the evaluation of safe reinforcement learning (RL) agents. Built using the MuJoCo XLA physics engine, CRAX offers up to a 100x speedup compared to…
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Gemma-3 270M fine-tuned to control robot with natural language commands
A developer has fine-tuned Google's Gemma-3 270M language model to control a simulated robot. The model was trained to translate natural language commands into JSON instructions for movement and object manipulation with…
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Energy conservation improves modular neural network robustness
Researchers have developed a novel method to improve the robustness of modular neural networks by enforcing energy conservation at module boundaries. This approach ensures that the activation energy, defined as the squa…
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New AI controllers run on low-power hardware with minimal bits
Researchers have developed new methods for creating efficient reinforcement learning controllers that can run on low-power hardware. One approach, "Learning Quantized Continuous Controllers," uses quantization-aware tra…
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New MuJoCo-Drones-Gym simulator enhances multi-drone RL research
Researchers have developed MuJoCo-Drones-Gym, an open-source simulation environment for multi-drone reinforcement learning. Built on the MuJoCo physics engine, it offers GPU acceleration and supports flexible physics mo…
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NTU team generates simulation-ready 3D assets from single images
Researchers from Nanyang Technological University (NTU) have developed PhysX-Anything, a system capable of generating physically simulated 3D assets from a single image. This advancement moves beyond merely creating vis…
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Tsinghua AIR releases UniLab for 10x faster robot training
Researchers from Tsinghua University's AIR DISCOVER Lab have introduced UniLab, an open-source framework for robot reinforcement learning training. This new architecture utilizes a heterogeneous approach, offloading phy…
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Tianji Intelligent to Showcase New Gento Robots at ICRA 2026
Tianji Intelligent has announced it will be a Platinum Partner at ICRA 2026 in Vienna, showcasing its new Gento series of force-controlled humanoid robot platforms. The company will feature three distinct models: Gento …
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Interstellar Light Year to debut GAIA Hand 20 at ICRA 2026
Shenzhen Interstellar Light Year Technology will showcase its GAIA Hand 20, a modular robotic hand with 20 degrees of freedom, at the ICRA 2026 conference in Vienna. The hand features a highly anthropomorphic design, op…
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New Framework Visualizes Locomotion Phases in AI Control
Researchers have developed a new framework to visualize latent motion phase structures within deep reinforcement learning (DRL) policies for locomotion control. This method extends clustering features beyond just state …
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Q-learning integration boosts offline In-Context RL performance
A new research paper explores the effectiveness of integrating Reinforcement Learning (RL) objectives into offline In-Context Reinforcement Learning (ICRL) methods. Experiments across over 150 datasets in GridWorld and …
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New visual RL method slashes training time and compute needs
Researchers have developed a new method called the stochastic decoupled policy gradient (SDPG) for efficient on-policy visual reinforcement learning. This technique trains visuomotor control policies end-to-end rapidly,…
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New RL policies boost efficiency with one-step generative control
Researchers have developed new methods for reinforcement learning policies that aim to improve efficiency and expressiveness. One approach, Score-Based One-step MeanFlow Policy Optimization (SOM), constructs a target ve…
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DIY Star Wars BDX droid learns to walk via reinforcement learning
An individual has recreated a Star Wars droid, known as the BDX, using a custom build that incorporates reinforcement learning for its movement. The project significantly reduced costs by using QDD motors and a repurpos…
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asRoBallet uses friction-aware RL for zero-shot Sim2Real transfer on ballbots
Researchers have developed asRoBallet, a novel end-to-end reinforcement learning policy for a humanoid ballbot, addressing the significant sim-to-real transfer gap in robotics. The system utilizes a high-fidelity MuJoCo…
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GLiBRL advances Deep Bayesian RL with tractable inference and better generalization
Researchers have developed GLiBRL, a novel approach for Bayesian Reinforcement Learning that enhances generalization by explicitly incorporating Bayesian task parameters. This method overcomes limitations of prior deep …
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Researchers fix synthetic data failures in reinforcement learning policy optimization
Researchers have identified and addressed algorithmic failures in Model-Based Policy Optimization (MBPO), a technique used in reinforcement learning. The study found that MBPO can underperform compared to other methods …