Results for "physical agents"

A system that perceives state, selects actions, and pursues goals—often combining LLM reasoning with tools and memory.

Field combining mechanics, control, perception, and AI to build autonomous machines.

Modeling interactions with environment.

Motion of solid objects under forces.

Systems where failure causes physical harm.

Modeling chemical systems computationally.

A broader capability to infer internal system state from telemetry, crucial for AI services and agents.

Coordination arising without explicit programming.

Agent calls external tools dynamically.

Interleaving reasoning and tool use.

Agent reasoning about future outcomes.

Agents optimize collective outcomes.

Agents have opposing objectives.

Market reacting strategically to AI.

Collective behavior without central control.

Agents copy others’ actions.

The text (and possibly other modalities) given to an LLM to condition its output behavior.

A high-priority instruction layer setting overarching behavior constraints for a chat model.

Expected cumulative reward from a state or state-action pair.

Continuous cycle of observation, reasoning, action, and feedback.

Learning from data generated by a different policy.

Separates planning from execution in agent architectures.

Models evaluating and improving their own outputs.

External sensing of surroundings (vision, audio, lidar).

Using production outcomes to improve models.

Randomizing simulation parameters to improve real-world transfer.

Predicts next state given current state and action.

Directly optimizing control policies.

Modifying reward to accelerate learning.

Reward only given upon task completion.