The field of robotics is witnessing significant advancements in embodied intelligence, with a focus on enabling robots to learn, reason, and adapt in complex environments. Recent developments have centered around improving the ability of robots to understand and interact with their surroundings, with a emphasis on long-horizon manipulation tasks. This has led to the creation of novel frameworks and architectures that integrate perception, planning, and control, allowing robots to perform tasks that require precise execution and robust error recovery. Notable advancements include the development of large language models that can reason about object parts and relationships, as well as the introduction of self-supervised data curation methods to improve the performance of imitation learning policies.

Some noteworthy papers in this area include: Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space, which introduces a hierarchical data collection space for robotic imitation learning, allowing for more efficient and effective learning of long-horizon manipulation tasks. SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning, which presents a self-supervised data curation framework that filters out low-quality state-action pairs to improve the performance of imitation learning policies. Agentic Robot: A Brain-Inspired Framework for Vision-Language-Action Models in Embodied Agents, which proposes a brain-inspired framework that addresses the limitations of current approaches through Standardized Action Procedures, establishing structured workflows for planning, execution, and verification phases.