COAD: Constant-Time Planning for Continuous Goal Manipulation with Compressed Library and Online Adaptation

Researchers from the University of Southern California and the University of Pennsylvania have introduced COAD (Constant-Time Planning for Continuous Goal Manipulation with Compressed Library and Online Adaptation), a breakthrough framework that solves a fundamental robotics bottleneck: repeated motion planning for similar tasks. Traditional approaches require storing massive libraries of pre-computed solutions or running computationally expensive planning algorithms for each new goal location. COAD instead discretizes the continuous task space into Task Coverage Regions and solves only a few representative 'root' problems offline, achieving library compression rates exceeding 99%.

At query time, when a robot needs to plan a motion to a new goal, COAD retrieves the nearest root solution in constant time. It then uses lightweight adaptation modules—like linear interpolation, Dynamic Movement Primitives (DMPs), or fast trajectory optimization—to modify the root motion to fit the exact new goal and obstacle configuration. This hybrid approach delivers sub-millisecond planning queries while maintaining path quality and success rates comparable to full planning, as validated in both simulation and real-world tests with various robotic manipulators.

The core innovation is the guarantee of coverage over a continuous parameter space without the storage overhead. By separating the expensive offline computation (solving root problems) from the ultra-fast online adaptation, COAD makes robots significantly more responsive and efficient in dynamic environments where goals change frequently. The open-source release of the code allows immediate integration and testing by the robotics community, potentially accelerating deployment in warehouses, manufacturing, and other repetitive manipulation settings.