MIT researchers developed FloatForm, a swarm of small aquatic robots that snap together like ants forming a raft, assembling into reconfigurable structures on the water.
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Researchers combined an efficient algorithm with dedicated hardware to rapidly generate 3D maps for navigation using minimal memory and power.
We’re releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for our research over the past year. We’ve used these environments to train models which work on physical robots.
We’ve created a robotics system, trained entirely in simulation and deployed on a physical robot, which can learn a new task after seeing it done once.
arXiv:2606. 08405v1 Announce Type: new Abstract: While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires an interpretable chain of reasoning that connects physical evidence to structured control architectures.
The paper introduces a self‑evolving scientific agent that uses large language models and iterative code generation to build interpretable, physically‑reasoned white‑box controllers for complex systems. The agent deploys candidate controllers in simulations, diagnoses dynamic behavior from multimodal evidence, and refines source code until a robust policy is achieved. Applied to a nonlinear fluid‑structure interaction problem—a two‑joint dogfish swimmer navigating an unsteady wake—the agent autonomously designs a controller that consistently reaches targets across a wide range of conditions without retraining.