AdaptPNP: Integrating Prehensile and Non-Prehensile Skills for Adaptive Robotic Manipulation

Non-prehensile (NP) manipulation, in which robots alter object states without forming stable grasps (for example, pushing, poking, or sliding), significantly broadens robotic manipulation capabilities when grasping is infeasible or insufficient. However, enabling a unified framework that generalizes across different tasks, objects, and environments while seamlessly integrating non-prehensile and prehensile (P) actions remains challenging: robots must determine when to invoke NP skills, select the appropriate primitive for each context, and compose P and NP strategies into robust, multi-step plans. We introduce ApaptPNP, a vision-language model (VLM)-empowered task and motion planning framework that systematically selects and combines P and NP skills to accomplish diverse manipulation objectives. Our approach leverages a VLM to interpret visual scene observations and textual task descriptions, generating a high-level plan skeleton that prescribes the sequence and coordination of P and NP actions. A digital-twin based object-centric intermediate layer predicts desired object poses, enabling proactive mental rehearsal of manipulation sequences. Finally, a control module synthesizes low-level robot commands, with continuous execution feedback enabling online task plan refinement and adaptive replanning through the VLM. We evaluate ApaptPNP across representative P&NP hybrid manipulation tasks in both simulation and real-world environments. These results underscore the potential of hybrid P&NP manipulation as a crucial step toward general-purpose, human-level robotic manipulation capabilities. Project Website: https://sites.google.com/view/adaptpnp/home

Audio-VLA: Adding Contact Audio Perception to Vision-Language-Action Model for Robotic Manipulation

The Vision-Language-Action models (VLA) have achieved significant advances in robotic manipulation recently. However, vision-only VLA models create fundamental limitations, particularly in perceiving interactive and manipulation dynamic processes. This paper proposes Audio-VLA, a multimodal manipulation policy that leverages contact audio to perceive contact events and dynamic process feedback. Audio-VLA overcomes the vision-only constraints of VLA models. Additionally, this paper introduces the Task Completion Rate (TCR) metric to systematically evaluate dynamic operational processes. Audio-VLA employs pre-trained DINOv2 and SigLIP as visual encoders, AudioCLIP as the audio encoder, and Llama2 as the large language model backbone. We apply LoRA fine-tuning to these pre-trained modules to achieve robust cross-modal understanding of both visual and acoustic inputs. A multimodal projection layer aligns features from different modalities into the same feature space. Moreover RLBench and LIBERO simulation environments are enhanced by adding collision-based audio generation to provide realistic sound feedback during object interactions. Since current robotic manipulation evaluations focus on final outcomes rather than providing systematic assessment of dynamic operational processes, the proposed TCR metric measures how well robots perceive dynamic processes during manipulation, creating a more comprehensive evaluation metric. Extensive experiments on LIBERO, RLBench, and two real-world tasks demonstrate Audio-VLA's superior performance over vision-only comparative methods, while the TCR metric effectively quantifies dynamic process perception capabilities.