Corki: An Algorithm-Architecture Co-Design Framework for Real-Time Embodied AI Robot Control

Conceptos Básicos

This paper introduces Corki, an algorithm-architecture co-design framework that enhances the real-time performance of embodied AI robots by predicting future trajectories to reduce LLM inference frequency and utilizing a hardware accelerator for seamless control signal generation.

Resumen

• Bibliographic Information: Huang, Y., Hao, Y., Yu, B., Yan, F., Yang, Y., Min, F., ... & Gan, Y. (2024). Software-Hardware Co-Design For Embodied AI Robots. arXiv preprint arXiv:2407.04292v4.

• Research Objective: This paper aims to address the real-time performance limitations of current embodied AI systems, which hinder their practical application in real-world scenarios.

• Methodology: The researchers propose Corki, a novel framework that decouples LLM inference, robotic control, and data communication. The algorithm predicts future robot trajectories instead of frame-by-frame actions, reducing LLM inference frequency. A hardware accelerator translates these trajectories into real-time control signals, and a pipelined system architecture hides communication latency. The framework is evaluated using the RoboFlamingo system and the Calvin dataset.

• Key Findings: Corki achieves significant performance improvements, including up to 3.6x speedup and a 17.3% increase in success rate compared to the baseline RoboFlamingo system. The hardware accelerator demonstrates low resource consumption, making it suitable for real-world deployment.

• Main Conclusions: Corki effectively addresses the real-time constraints of embodied AI systems by reducing LLM inference frequency and optimizing the control process. The proposed algorithm-architecture co-design framework offers a promising solution for deploying embodied AI robots in real-world applications.

• Significance: This research significantly contributes to the field of embodied AI and robotics by addressing the critical challenge of real-time control. The proposed framework paves the way for developing more efficient and responsive embodied AI systems, enabling their wider adoption in various domains.

• Limitations and Future Research: The paper acknowledges that the current implementation of Corki focuses on a specific robotic arm and dataset. Future work aims to explore the framework's generalizability across different robot platforms and more complex tasks. Additionally, investigating more sophisticated trajectory prediction algorithms and exploring alternative hardware acceleration strategies could further enhance the system's performance.

Estadísticas

Corki reduces LLM inference frequency by up to 8.0×, resulting in up to 3.6× speed up.
The maximum success rate improvement is 17.3% higher than the baseline.
LLM inference takes 76.9% of the execution time in the baseline system.
Robot control takes 4.1% of the execution time in the baseline system.
Data communication takes 19.0% of the execution time in the baseline system.
LLM inference accounts for 98.0% of the total energy consumption in the baseline system.
Robot control and data communication account for 2.0% of the total energy consumption in the baseline system.
Corki accelerator consumes only 13.6% of digital signal processors (DSP), 7.8% of flip-flops (FF), and 16.9% of look-up tables (LUT).
Corki accelerator utilizes only 6.6% of the total block random access memory (BRAM).
Over 51% of matrix updates can be avoided in the control process without any loss in control accuracy using the proposed approximate computing method.
Corki reduces the trajectory error by 25.0% on average compared to the baseline.

Citas

"The single most important difference between using LLMs for generating text and images versus integrating them as decision-making and planning modules within robotic pipelines lies in the hard real-time constraints imposed on robots."
"Today’s embodied AI pipeline is designed purely based on the convenience of algorithm designers as executing frame by frame sequentially is a traditional method in video processing algorithms, yet it violates a basic principle of robotic software design."
"Critically, the unbalanced frequency requirements existing in robotic software stack allow us to decouple LLM inference, robotic control and data communication."

Ideas clave extraídas de

Software-Hardware Co-Design For Embodied AI Robots

by Yiyang Huang... a las arxiv.org 11-19-2024

https://arxiv.org/pdf/2407.04292.pdf

Consultas más profundas

How can Corki be adapted to handle dynamic environments with unpredictable changes, such as moving obstacles or human interaction?

Corki's trajectory prediction framework offers a solid foundation for handling dynamic environments, but it needs enhancements to address unpredictable changes like moving obstacles or human interaction. Here's a breakdown of potential adaptations:
1. Integrating Real-time Perception:

Faster Sensor Fusion: Corki currently relies on images captured at 30Hz. Increasing the sensor acquisition rate (e.g., using faster cameras, depth sensors) would provide more frequent updates about the environment.
Multi-Modal Perception: Incorporating data from various sensors like LiDAR, ultrasonic sensors, or tactile sensors can provide a richer understanding of the environment, enabling detection of obstacles not apparent in images alone.
Reactive Planning: Integrating a reactive planning layer on top of Corki's trajectory prediction would allow for quick adjustments to avoid collisions. This could involve techniques like Dynamic Window Approach (DWA) or Timed Elastic Bands (TEB) that rapidly generate safe trajectories based on real-time obstacle information.
2. Enhancing Corki's Algorithm:

Shorter Prediction Horizons: In highly dynamic environments, relying on long trajectories might be detrimental. Corki could dynamically adjust the prediction horizon based on the perceived dynamism of the environment. Shorter horizons in rapidly changing situations would make the robot more responsive.
Predicting Obstacle Motion: Instead of just reacting to obstacles, Corki could be extended to predict their future trajectories. This would require incorporating obstacle motion models and learning algorithms that can anticipate their behavior.
Human-Robot Collaboration: For human interaction, Corki could be trained on datasets containing human movements and learn to predict human intentions. This would enable the robot to anticipate human actions and collaborate more effectively.
3. Learning-Based Adaptation:

Reinforcement Learning: Training Corki in simulated environments with moving obstacles and human agents using Reinforcement Learning (RL) can teach it to generate robust trajectories in dynamic scenarios.
Imitation Learning: Learning from demonstrations of humans navigating similar dynamic environments can provide valuable data for Corki to adapt its trajectory prediction.
Challenges:

Computational Complexity: Real-time perception and reactive planning add significant computational burden, requiring efficient algorithms and potentially more powerful hardware.
Data Requirements: Training Corki for dynamic environments necessitates large and diverse datasets encompassing various obstacle motions and human behaviors.
Safety Considerations: Ensuring robot safety in dynamic environments is paramount. Rigorous testing and validation are crucial to prevent collisions and ensure safe human-robot interaction.

While reducing LLM inference frequency improves efficiency, could it potentially limit the robot's ability to react to subtle cues or changes in the environment that occur between predictions?

You're right to point out the potential trade-off between efficiency gains from reduced LLM inference frequency and the robot's responsiveness to subtle environmental cues. Here's a deeper look at this issue:
Limitations of Reduced Inference:

Missed Cues: Infrequent LLM calls might cause the robot to miss subtle changes in the environment that occur between predictions. For example, a slight shift in an object's position or a change in a person's expression might go unnoticed.
Delayed Reactions: Even if changes are detected, the delay introduced by the infrequent LLM inference could lead to slower reaction times. This could be problematic in situations requiring swift responses, such as avoiding sudden obstacles.
Mitigation Strategies:

Hybrid Control Architectures: Combine Corki's trajectory prediction with a reactive control layer. The reactive layer, operating at a higher frequency, can handle immediate responses to subtle cues, while Corki provides longer-term planning.
Event-Triggered Inference: Instead of fixed intervals, trigger LLM inference dynamically based on events detected by the robot's sensors. For example, a sudden change in sensor readings could trigger an LLM call to re-plan the trajectory.
Adaptive Prediction Horizons: As mentioned earlier, dynamically adjusting the prediction horizon based on the environment's dynamism can help. In situations requiring high responsiveness, shorter horizons with more frequent LLM calls can be used.
Local Processing: Offload some of the perception and decision-making tasks to local processing units on the robot. This can enable faster reactions to subtle cues without relying solely on the LLM.
Balancing Act:
Finding the right balance between efficiency and responsiveness is crucial. The optimal inference frequency depends on the specific application and the level of dynamism in the environment. In highly dynamic scenarios, more frequent LLM calls might be necessary, even at the cost of some efficiency.

If we view the evolution of robot control as a form of language, how might Corki's approach to trajectory prediction influence the development of more intuitive and expressive human-robot communication methods?

Viewing robot control through the lens of language is an intriguing concept. Corki's trajectory prediction, in this context, could be a stepping stone towards a more intuitive and expressive form of human-robot communication. Here's how:
1. From Explicit Commands to Implicit Intent:

Traditional Robot Control: Relies on explicit, low-level commands (e.g., "move joint 1 by 10 degrees"). This is akin to communicating with a robot in a rudimentary machine language.
Corki's Trajectory Prediction: Enables the robot to understand and execute more complex actions represented by trajectories. This is a step towards a higher-level language where humans can convey intent rather than specific instructions.
2. Towards Natural Language-Based Control:

Trajectory as a Communication Medium: Trajectories can be seen as a form of spatial language. By demonstrating desired trajectories, humans can intuitively convey their intentions to robots.
Learning from Human Demonstrations: Corki's ability to learn from trajectories makes it well-suited for imitation learning. Robots can learn new tasks by observing human demonstrations, leading to more natural teaching methods.
3. Expressiveness and Nuance in Robot Actions:

Smooth and Efficient Movements: Trajectory-based control allows for smoother and more human-like robot movements, enhancing the expressiveness of their actions.
Anticipating Human Needs: By learning from human trajectories, robots can anticipate human actions and proactively adapt their behavior, leading to more seamless collaboration.
4. Challenges and Future Directions:

Semantic Understanding: Current trajectory prediction focuses on spatial aspects. Integrating semantic understanding (e.g., recognizing objects, understanding actions) is crucial for truly intuitive communication.
Feedback and Dialogue: Developing mechanisms for robots to provide feedback on their understanding of trajectories and engage in a dialogue with humans is essential for effective communication.
Ethical Considerations: As robots become more expressive and autonomous, addressing ethical considerations related to human control and robot decision-making becomes increasingly important.
Conclusion:
Corki's trajectory prediction, while primarily focused on efficiency, has the potential to influence the development of more intuitive and expressive human-robot communication. By bridging the gap between low-level commands and high-level intent, trajectory-based control paves the way for a future where humans and robots can interact and collaborate more naturally and effectively.