Challenges and Future of EndtoEnd Robotic Systems

From: hu-po

End-to-end robotic systems are a novel approach to robotic control that integrates perception and action within a single model, contrasting with traditional modular architectures like ROS [03:57:00], [12:21:00].

Characteristics of End-to-End Control

An end-to-end system means that the pipeline flows directly from input (images, sensors) to output (robot joint commands or control signals) [03:57:00], [04:00:00], [04:09:00]. This unified approach aims to leverage the benefits of large-scale pre-training on language and vision data from the web [05:33:00].

Key aspects include:

• Vision-Language Action Models (VLAMs) The RT-2 model, for instance, is a vision-language action model that combines text and images with robot actions [01:07:00], [06:55:00], [26:22:00].
• Action Tokenization Robot actions (like positional and rotational displacements) are expressed as text tokens and incorporated directly into the model’s training set, similar to natural language tokens [06:25:05], [06:39:00], [25:43:00], [46:42:00]. This allows the model to output actions using the same unified space as natural language responses [40:17:00].
• Co-fine-tuning A crucial training detail involves co-fine-tuning robotics data with original web-scale data [56:46:00]. This prevents “catastrophic forgetting” of the broad, pre-trained knowledge when specializing in robotic tasks [57:55:00].

Benefits and Emergent Capabilities

Integrating large, pre-trained Vision Language Models (VLMs) directly into low-level robotic control offers significant advantages:

• Improved Generalization These systems can generalize to novel objects, unseen backgrounds, and environments that were not present in their specific robot training data [04:21:00], [08:23:00], [28:20:00], [30:30:00], [31:36:00], [34:49:00]. This marks a substantial improvement over previous methods that struggled with even slight changes in environment [12:55:00], [13:51:00], [14:03:00], [16:05:00], [11:51:00], [11:58:00].
• Emergent Semantic Reasoning VLMs enable robots to interpret complex commands and perform rudimentary reasoning, leveraging common sense knowledge from internet-scale data [04:35:00], [08:20:00], [28:24:00], [11:12:00]. Examples include:
  • Picking the smallest or largest object, or the one closest to another [08:35:00].
  • Repurposing pick-and-place skills for semantically indicated locations [28:52:00].
  • Interpreting numerical cues (e.g., “pick up the third chip”) or relations between objects [29:02:00].
  • Recognizing celebrities or understanding commands like “move the Coke can to the person with glasses” [45:57:00], [01:21:30].
• Chain-of-Thought Prompting Incorporating Chain of Thought prompting allows the model to perform multi-stage semantic reasoning, effectively planning its actions in natural language before execution [08:40:00], [29:15:00], [01:27:51].

Challenges and Limitations

Despite the advancements, significant challenges remain for end-to-end robotic systems:

• Computational Costs and Inference Speed Large models (e.g., 55 billion parameters) are computationally intensive and cannot run directly on standard robot GPUs for real-time control [42:03:00], [01:00:39]. While they can run in a multi-TPU cloud service, this introduces latency [01:02:17].
• Limited Action Generalization The primary limitation is that these models do not acquire new abilities to perform new physical motions [29:41:00], [01:16:33]. Their physical skills remain constrained to the distribution of actions seen in their training data (e.g., typically pick-down movements) [30:15:00], [01:29:30].
• Discretization of Action Space Continuous robot actions must be discretized into a fixed number of bins (e.g., 256 for each dimension) to be treated as tokens, which can introduce limitations [48:25:00], [49:52:00].
• Evaluation Overhead Real-world robot evaluation trials are time-consuming and labor-intensive, requiring manual environment resets [07:48:00].
• Fragility to External Variables While improved, systems can still be sensitive to environmental changes like lighting, requiring careful interweaving of evaluations [02:00:00].
• Data Acquisition for New Skills Overcoming action generalization requires training on much more varied skill data, potentially including videos of humans performing diverse actions [01:29:41], [01:29:52].

Future Outlook and Predictions

The future of end-to-end robotic systems holds several exciting possibilities:

• Ubiquitous Home Robots The ability to generalize in semantic and visual understanding, combined with hardware that has been available for some time, suggests that generalist home robots could become widespread within five to ten years [01:06:00], [01:01:32], [01:35:02], [01:45:50].
• Advancements in Hardware and Software Innovations in quantization and distillation techniques are expected to enable models to run at higher rates on lower-cost hardware, potentially even on devices like cell phones [01:01:26], [01:01:57], [01:30:47].
• Cloud Robotics A shift towards cloud-based inference for robots could circumvent local computational limitations, with increasing internet speeds reducing latency issues [01:04:55], [01:30:55]. This approach also allows for greater control over model parameters.
• Leapfrogging Autonomous Vehicles Some predict that autonomous home robots will become commonplace before self-driving cars, largely due to the less stringent safety requirements for slow-moving, smaller home robots compared to high-speed vehicles [01:40:40], [01:40:51]. Autonomous vehicles are perceived as “stuck” in older, modular development patterns with strict safety guarantees for each component [01:40:51].
• Role of Vision Language Models Language models (and VLMs specifically) are seen as the “intelligence” that robotics has been missing, providing the necessary generalist capabilities [01:34:44].
• Data Privacy Concerns The potential for continuous or lifelong learning, where robots gather data from homes, raises questions about privacy (e.g., sending images to cloud services) [01:36:05].
• Open Source vs. Proprietary Models There is an internal desire among researchers for more open-source models, despite current industry trends leaning towards proprietary control over advanced AI [01:31:38].

In conclusion, end-to-end robotic systems, powered by advanced Vision Language Models, are poised to revolutionize robotics by significantly enhancing generalization and enabling complex reasoning. While challenges related to computational efficiency and full action-space generalization remain, ongoing research and technological advancements suggest a future where intelligent, capable robots are an integral part of daily life [01:34:40], [01:45:46].