NVIDIA's Robotics Research and Development Digest (R²D²) highlights advancements in robot manipulation through perception-based and GPU-accelerated Task and Motion Planning (TAMP) for long-horizon tasks. Traditional TAMP systems often struggle in dynamic environments due to their reliance on static models. NVIDIA's research addresses this limitation by integrating perception to enable robots to update plans mid-execution and adapt to changing scenarios. Key research areas include:

OWL-TAMP: Integrates vision-language models (VLMs) with TAMP to enable robots to execute complex tasks described in natural language, such as "put the orange on the table."
VLM-TAMP: Combines VLMs with traditional TAMP to generate and refine action plans in visually rich environments, allowing robots to handle ambiguous information and improve performance in complex manipulation tasks.
NOD-TAMP: Uses neural object descriptors (NODs) derived from 3D point clouds to help generalize object types, enabling robots to interact with new objects and adapt actions dynamically.
cuTAMP: Accelerates robot planning with GPU parallelization, significantly reducing the time required to solve continuous variables in TAMP, enabling solutions for packing, stacking, or manipulating many objects in seconds.
Fail2Progress: A framework that enables robots to learn from their failures using Stein variational inference to generate targeted synthetic datasets, improving skill models through data-driven correction and simulation-based refinement.

These research efforts aim to overcome challenges in traditional TAMP by leveraging vision, language, and GPU acceleration to enhance robot adaptability, learning, and performance in complex manipulation tasks. Key Concepts:

Subgoals: Smaller intermediate objectives that guide the robot step-by-step toward the final goal.
Affordances: Actions that an object or environment allows a robot to perform, based on its properties and context.
Differentiable Constraints: Physical limits (like joint angles, collision avoidance) in robot-motion planning that are adjustable via learning and efficiently computed on GPUs.

These innovations contribute to making long-horizon problem-solving feasible in real-world robotics applications. Source: [NVIDIA Technical Blog](https://developer.nvidia.com/blog/r2d2-perception-guided-task-motion-planning-for-long-horizon-manipulation/)