Traditional task and motion planning (TAMP) systems for robot manipulation use cases operate on static models that always fail in latest environments. Integrating perception with manipulation is an answer to this challenge, enabling robots to update plans mid-execution and adapt to dynamic scenarios.
On this edition of the NVIDIA Robotics Research and Development Digest (R²D²), we explore using perception-based TAMP and GPU-accelerated TAMP for long-horizon manipulation. We’ll also study a framework for improving robot manipulation skills. And we’ll show how vision and language will be used to translate pixels into subgoals, affordances, and differentiable constraints.
- Subgoals are smaller intermediate objectives that guide the robot step-by-step toward the ultimate goal.
- Affordances describe the actions that an object or environment allows a robot to perform, based on its properties and context. As an illustration, a handle affords “grasping,” a button affords “pressing,” and a cup affords “pouring.”
- Differentiable constraints in robot-motion planning make sure that the robot’s movements satisfy physical limits (like joint angles, collision avoidance, or end-effector positions) while still being adjustable via learning. Because they’re differentiable, GPUs can compute and refine them efficiently during training or real-time planning.
How task and motion planning transforms vision and language into robot motion
TAMP involves deciding what a robot should do and how it should move to do it. Doing this requires combining high-level task-planning (what task to do) and low-level motion-planning (learn how to move to perform the duty).
Modern robots can use each vision and language (like pictures and directions) to interrupt down complex tasks into smaller steps, called subgoals. These subgoals help the robot understand what must occur next, what objects to interact with, and learn how to move safely.
This process uses advanced models to show images and written instructions into clear plans the robot can follow in real-world situations. Long-horizon manipulation requires structured intentions that will be satisfied by the planner. Let’s see how OWL-TAMP, VLM-TAMP, and NOD-TAMP help address this:
- OWL-TAMP: This workflow enables robots to execute complex, long-horizon manipulation tasks described in natural language, comparable to “put the orange on the table.” OWL-TAMP is a hybrid workflow that integrates vision-language models (VLMs) with TAMP, where the VLM generates constraints that describe learn how to ground open-world language (OWL) instructions in robot motion space. These constraints are incorporated into the TAMP system, which ensures physical feasibility and correctness through simulation feedback.
- VLM-TAMP: This can be a workflow for planning multi-step tasks for robots in visually wealthy environments. VLM-TAMP combines VLMs with traditional TAMP to generate and refine motion plans in real-world scenes. It uses a VLM to know images and uses task descriptions (like “make chicken soup”) to generate high-level plans for the robot. These plans are then iteratively refined through simulation and motion planning to envision feasibility. This hybrid model outperforms each the VLM-only and TAMP-only baselines on long-horizon kitchen tasks that require 30 to 50 sequential actions and involve as much as 21 different objects. This workflow enables robots to handle ambiguous information through the use of each visual and language context, leading to improved performance in complex manipulation tasks.
- NOD-TAMP: Traditional TAMP frameworks often struggle to generalize on long-horizon manipulation tasks because they depend on explicit geometric models and object representations. NOD-TAMP overcomes this through the use of neural object descriptors (NODs) to assist generalize object types. NODs are learned representations derived from 3D point clouds that encode spatial and relational properties of objects. This permits robots to interact with latest objects and helps the planner adapt actions dynamically.
How cuTAMP accelerates robot planning with GPU parallelization
Classical TAMP first analyzes the outline of actions for a task (called plan skeletons) after which proceeds to unravel the continual variables. This second step will likely be the bottleneck in manipulation systems, which is vastly accelerated in cuTAMP. For a specified skeleton in cuTAMP, hundreds of seeds (particles) are sampled, after which differentiable batch optimization is executed on the GPU to satisfy the assorted constraints (like inverse kinematics, collisions, stability, and goal costs).
If a skeleton just isn’t feasible, the algorithm backtracks. Whether it is, the algorithm provides a plan, which may often occur in a matter of seconds for constrained packing/stacking tasks. Which means robots can find solutions for packing, stacking, or manipulating many objects in seconds as an alternative of minutes or hours.
This “vectorized satisfaction” is the essence of creating long-horizon problem-solving feasible in real-world applications.
How robots learn from failures using Stein variational inference
Long-horizon manipulation models can fail in novel conditions not seen during training. Fail2Progress is a framework for improving manipulation by enabling robots to learn from their very own failures. This framework integrates failures into skill models through data-driven correction and simulation-based refinement. Fail2Progress uses Stein variational inference to generate targeted synthetic datasets just like observed failures.
These generated datasets can then be used to fine-tune and re-deploy a skill-effect model, enabling fewer repeats of the identical failure on long-horizon tasks.
Getting began
On this blog, we talked about perception-based TAMP, GPU-accelerated TAMP, and a simulation-based refinement framework for robot manipulation. We saw common challenges in traditional TAMP and the way these research efforts aim to unravel them.
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This post is an element of our NVIDIA Robotics Research and Development Digest (R2D2) to offer developers deeper insight into the most recent breakthroughs from NVIDIA Research across physical AI and robotics applications.
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Acknowledgments
For his or her contributions to the research mentioned on this post, due to Ankit Goyal, Caelan Garrett, Tucker Hermans, Yixuan Huang, Leslie Pack Kaelbling, Nishanth Kumar, Tomas Lozano-Perez, Ajay Mandlekar, Fabio Ramos, Shuo Cheng, Mohanraj Devendran Shanthi, William Shen, Danfei Xu, Zhutian Yang, Novella Alvina, Dieter Fox, and Xiaohan Zhang.
