# Dynamics-Compliant Trajectory Diffusion for Super-Nominal Payload Manipulation

## TL;DR

This paper introduces a novel trajectory planning method using diffusion models that allows articulated robots to safely handle payloads significantly above nominal ratings across large workspace regions, improving utilization.

## Contribution

It presents a diffusion-based trajectory generation approach that explicitly incorporates payload constraints, enabling real-time, feasible motion planning beyond traditional limits.

## Key findings

- Up to 67.6% of workspace accessible with payloads over 3x nominal capacity
- The method generates dynamically feasible trajectories in constant time
- Experimental validation on a 7 DoF robot demonstrates significant payload handling improvements

## Abstract

Nominal payload ratings for articulated robots are typically derived from worst-case configurations, resulting in uniform payload constraints across the entire workspace. This conservative approach severely underutilizes the robot's inherent capabilities -- our analysis demonstrates that manipulators can safely handle payloads well above nominal capacity across broad regions of their workspace while staying within joint angle, velocity, acceleration, and torque limits. To address this gap between assumed and actual capability, we propose a novel trajectory generation approach using denoising diffusion models that explicitly incorporates payload constraints into the planning process. Unlike traditional sampling-based methods that rely on inefficient trial-and-error, optimization-based methods that are prohibitively slow, or kinodynamic planners that struggle with problem dimensionality, our approach generates dynamically feasible joint-space trajectories in constant time that can be directly executed on physical hardware without post-processing. Experimental validation on a 7 DoF Franka Emika Panda robot demonstrates that up to 67.6% of the workspace remains accessible even with payloads exceeding 3 times the nominal capacity. This expanded operational envelope highlights the importance of a more nuanced consideration of payload dynamics in motion planning algorithms.