Improving the performance of AI-powered Affordable Robotics for Assistive Tasks

Abstract

By 2050, the global demand for assistive care is expected to reach 3.5 billion people, far outpacing the availability of human caregivers. Existing robotic solutions remain expensive and require technical expertise, limiting accessibility. This work introduces a low-cost robotic arm for assistive tasks such as feeding, cleaning spills, and fetching medicine. The system uses imitation learning from demonstration videos, requiring no task-specific programming or manual labeling. The robot consists of six servo motors, dual cameras, and 3D-printed grippers. Data collection via teleoperation with a leader arm yielded 50,000 video frames across the three tasks. A novel Phased Action Chunking Transformer (PACT) captures temporal dependencies and segments motion dynamics, while a Temporal Ensemble (TE) method refines trajectories to improve accuracy and smoothness. Evaluated across five model sizes and four architectures, with ten hours of real-world testing, the system achieved over 90% task accuracy, up to 40% higher than baselines. PACT enabled a 5x model size reduction while maintaining 75% accuracy. Saliency analysis showed reliance on key visual cues, and phase token gradients peaked at critical trajectory moments, indicating effective temporal reasoning. Future work will explore bimanual manipulation and mobility for expanded assistive capabilities.