Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents (2404.08825v2)

Abstract: This paper introduces a novel zero-shot motion planning method that allows users to quickly design smooth robot motions in Cartesian space. A B\'ezier curve-based Cartesian plan is transformed into a joint space trajectory by our neuro-inspired inverse kinematics (IK) method CycleIK, for which we enable platform independence by scaling it to arbitrary robot designs. The motion planner is evaluated on the physical hardware of the two humanoid robots NICO and NICOL in a human-in-the-loop grasping scenario. Our method is deployed with an embodied agent that is a LLM at its core. We generalize the embodied agent, that was introduced for NICOL, to also embody NICO. The agent can execute a discrete set of physical actions and allows the user to verbally instruct various different robots. We contribute a grasping primitive to its action space that allows for precise manipulation of household objects. The updated CycleIK method is compared to popular numerical IK solvers and state-of-the-art neural IK methods in simulation and is shown to be competitive with or outperform all evaluated methods when the algorithm runtime is very short. The grasping primitive is evaluated on both NICOL and NICO robots with a reported grasp success of 72% to 82% for each robot, respectively.

• The paper presents CycleIK, a novel neuro-inspired inverse kinematics method integrating Bézier curve motion planning for neuro-robotic grasping with humanoid agents.
• CycleIK achieved grasp success rates between 72% and 82% on physical humanoid robots, matching or exceeding the performance of existing numerical and neural IK solvers.
• The system incorporates an LLM-driven embodied agent that interprets verbal instructions for grasping tasks, enhancing human-robot collaboration.

Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents

The paper "Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents" presents a novel approach that combines advanced inverse kinematics (IK) solutions with motion planning to facilitate neuro-robotic grasping using humanoid robots. The significance of this research lies in its proposal of CycleIK, a neuro-inspired IK method that integrates seamlessly with a motion planner based on Bézier curves, a methodology known for its smooth trajectory generation in Cartesian space.

Overview of the Method

CycleIK is a zero-shot motion planner that converts Cartesian space plans derived from Bézier curves into joint space trajectories. The proposed method is distinct from classical numerical IK solvers and state-of-the-art neural IK methods by enabling scalable, platform-independent application across various robot designs. The deployment of this method is particularly significant as it is tested on two humanoid robots, NICO and NICOL, within a human-in-the-loop grasping scenario. An embodied agent powered by a LLM interprets verbal user instructions and facilitates the execution of grasping tasks, evidencing a flexible and user-friendly human-robot interaction paradigm.

Technical Contributions

1. CycleIK Performance Evaluation: The paper validates CycleIK against existing numerical IK solvers, such as BioIK and Trac-IK, and advanced neural approaches like IKFlow. The results indicate that CycleIK matches or exceeds the performance of these methods, particularly in scenarios requiring rapid execution, showcasing low algorithmic runtime and high precision. For instance, grasping experiments on physical hardware achieved grasp success rates between 72% and 82%, thereby demonstrating practical efficacy.
2. Bézier Curve Integration: The use of Bézier curves for motion planning provides a smooth transition from initial pose to target pose in Cartesian space. This approach addresses the limitations of direct joint space manipulation by ensuring that generated motions are fluid and adaptable to varying degrees of freedom inherent in redundant kinematic chains.
3. Neuro-Inspired IK Solver: By replacing conventional loss functions with those derived from the Smooth L1 loss, CycleIK gains enhanced stability and precision in IK solutions. This change is critical for maintaining computational efficiency while ensuring accuracy in pose estimations.
4. Embodied Agent Functionality: The integration of an LLM-driven embodied agent enhances user interaction through natural language processing capabilities, as it allows real-time verbal instructions for executing discrete physical actions. Additionally, the incorporation of object detection (via ViLD) and speech recognition (via Whisper) systems underpins a comprehensive human-robot collaborative framework.

Implications and Future Directions

The practical implications of this work are profound, providing a robust framework for advanced neuro-robotic applications. The ability to deploy CycleIK on multiple robot platforms signifies its adaptability and potential for future developments in domains requiring rapid, precise, and user-friendly robotic interaction paradigms. The success of real-world applications, such as grasping tasks in domestic environments, highlights the method's potential to enhance everyday human-robot interactions.

Future research may explore further enhancements in the neuro-inspired architectures and fine-tuning of Bézier-based planners for dynamic environments. Exploring obstacle avoidance within the planning phase and expanding the action space of the embodied agent could improve operational safety and application scope. Moreover, extending the language interaction capabilities and object manipulation dexterity will bolster the use of humanoid robots in more complex and unstructured settings.

Overall, this research contributes a significant step forward in deploying sophisticated AI-driven planning techniques within physical systems, thereby fostering more intuitive and seamless human-robot collaboration.