Autonomous-Agents is a research-focused repository that collects implementations, experiments, and academic resources related to autonomous multi-agent systems and intelligent robotics. The project explores how multiple agents can cooperate and interact with complex environments through machine learning, imitation learning, and multimodal sensing. It includes frameworks that integrate visual perception, tactile sensing, and spatial reasoning to guide the actions of robotic agents during manipulation or collaborative tasks. One of the central concepts explored in the repository is the integration of different sensory modalities using advanced machine learning techniques such as Feature-wise Linear Modulation and graph-based attention mechanisms. These methods allow agents to combine visual and geometric information while maintaining awareness of the spatial relationships between agents and objects.
Features
- Multi-agent learning frameworks for cooperative robotic tasks
- Integration of visual, tactile, and geometric sensor inputs
- Graph attention mechanisms for spatial reasoning among agents
- Diffusion-based action decoding for robotic manipulation
- Adaptive attention models that adjust sensory weighting during tasks
- Experimental implementations for autonomous learning and agent coordination