MuJoCo
PulseAugur coverage of MuJoCo — every cluster mentioning MuJoCo across labs, papers, and developer communities, ranked by signal.
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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 …
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omicro Flux robot uses Claude Code and Cranq at AI expo
An exhibition showcasing generative AI was held on May 6, 2026, at the Sunrayce Hall. The event featured a spherical robot named "omicro Flux" integrated with the MuJoCo simulator and Claude Code. Other demonstrations i…
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New research explores ensemble models for improved AI performance and robustness
Two new research papers introduce novel methods for improving ensemble models in machine learning. The first, PACE, combines pruning and compression techniques to create more efficient and interpretable ensembles, outpe…
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New research explores causal models beyond global monotonicity and partial observations
Researchers have developed new frameworks for understanding causal relationships in complex systems, particularly when dealing with non-monotonicity and partial observability. One paper introduces non-monotone triangula…
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SAVGO algorithm uses geometry to improve reinforcement learning policy updates
Researchers have introduced SAVGO, a novel reinforcement learning algorithm designed to improve policy updates in continuous control tasks. SAVGO learns a joint state-action embedding space where similar action-value es…
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Tsinghua University releases GS-Playground for efficient embodied AI simulation
Researchers from Tsinghua University's AIR DISCOVER Lab have developed and open-sourced GS-Playground, a novel simulation framework designed to overcome bottlenecks in visual-centric embodied AI training. The framework …
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New C++ engine HASE achieves 33M steps/sec for multi-agent RL training
Researchers have developed a new C++ engine called Hide-And-Seek-Engine (HASE) designed to significantly improve the efficiency of training reinforcement learning agents in decentralized, partially observable environmen…
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New activation functions boost AI plasticity in continual learning
Researchers have developed new activation functions, Smooth-Leaky and Randomized Smooth-Leaky, to address the loss of plasticity in continual learning models. These functions are designed to maintain a model's ability t…
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Google AI unveils Nested Learning; OpenAI advances meta-learning and AI safety
Google Research has introduced "Nested Learning," a novel machine learning paradigm designed to address the challenge of catastrophic forgetting in continual learning. This approach views models as interconnected optimi…
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OpenAI finds evolution strategies rival reinforcement learning for AI training
OpenAI researchers have found that evolution strategies (ES), a decades-old optimization technique, can rival the performance of modern reinforcement learning (RL) methods on benchmarks like Atari and MuJoCo. ES offers …