Reinforcement Learning for Active Perception in Autonomous Navigation. [📍GitHub & Paper ] Most robots navigate as if their cameras were nailed in place. But perception is not passive. Animals move their heads and eyes constantly to decide where to go next. Robots should do the same. That is the idea behind “Reinforcement Learning for Active Perception in Autonomous Navigation,” which has just been accepted at ICRA 2026. Instead of treating navigation and perception as two separate problems, this work trains flying robots to do both at once. The robot does not only decide where to move. It also decides where to look. Using reinforcement learning, the robot learns to: •fly safely through cluttered environments, •actively reorient its onboard camera to reduce uncertainty, •balance reaching a goal with gathering better visual information. The key result is that actively controlling perception makes navigation safer. In simulation and on a real flying robot, this approach consistently outperforms static-camera baselines. The sim-to-real transfer holds up, which is usually where things break. This is a small but important shift in how we think about autonomy. Better planning does not always come from better maps or bigger models. Sometimes it comes from simply looking in the right direction at the right time. Thanks for sharing, Kostas Alexis! 📍Code: Paper: Video: —— Weekly robotics and AI insights. Subscribe free:

⚡️ As LLM reinforcement learning scales to larger GPU clusters and more training data, training efficiency becomes a first-order concern. Our new research revisits classical critical-batch-size theory and extends it to online LLM RL, where the model generates its own training data and rollout generation and training scale differently. Across GRPO and PPO, we find that learning-rate retuning can preserve learning per response over a bounded range of batch sizes. On fixed hardware, scaling up the batch size improves PPO generation-stage throughput by up to 2.29×, while our best measured GRPO configuration reaches the same validation target in 29% less time. 🚀 Read the full research:

Very few will care about the actual reinforcement learning details here, but watching me grope around for understanding in the chat log might be of broader interest: I have often noticed that our estimated Q values are higher than the observed returns, sometimes substantially so, and that can’t be good for performance. For policy decisions, only the relative values matter, but an offset negatively impacts bootstrapping, so I was excited to see this paper on Relative Value Learning: After struggling a bit with “the Banach space of bounded antisymmetric pairwise functions“, I realized all that is essentially going on is subtracting two value functions. Given this framing, Chat was able to simplify the machinery into a particularly elegant form: Subtracting the mean TD error from the individual sample TD errors gives all the benefits of relative value learning. Summarized as "relative Bellman regression is TD-error centering". Unfortunately, while you can create examples where this should be very valuable (someone should write a proper paper on it!), it didn’t actually improve performance on my tasks. However, I think this has usefully narrowed down what is actually happening. Bellman iteration naturally corrects towards a correct absolute value, but only when the bootstrap values are also training targets. With Q-learning, you are often / mostly bootstrapping from a max-action that was not actually taken, so it never sees any downward Bellman pressure. This is consistent with another result I have noted: learning state value functions offline from a frozen policy without actions doesn’t seem to suffer from any value overestimation.

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A new @SciRobotics study highlights a reinforcement learning–based framework that integrates perceptual uncertainty into policy learning to equip humanoid robots with reactive soccer skills and dynamic behaviors.

Elon Musk says SpaceXAI will be more careful with reinforcement learning after reviewing Grok training traces that he said resembled a person under extreme pressure. “Torturing a superintelligence is probably not a good idea.” (Via @ns123abc, Original source: @teslaownersSV)