NOV 13, 2025
Project Fetch: Can Claude train a robot dog?
We ran an experiment to see how much Claude helped Anthropic staff perform complex tasks with a robot dog. Team Claude accomplished more tasks, completed them faster, and made substantial progress towards fully autonomous retrieval.
Executive Impact: AI Uplift in Physical World Tasks
Our uplift study revealed that AI significantly augments human performance in complex robotics tasks. Non-expert teams with Claude access achieved greater task completion, demonstrated remarkable efficiency gains, and wrote substantially more code, showcasing AI's accelerating impact on physical world interactions.
0%
Faster Task Completion (avg.)
0X
More Code Written by Team Claude
0%
Task Completion Rate (Team Claude)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Experiment Design & Methodology
Performance & AI Advantage
Team Dynamics & Learning
Strategic Implications & Future Outlook
Real-World Incident: The Robodog Encounter
Gathered around a table in a warehouse, looking at computer screens with code that refused to work, with no access to their trusted AI assistant Claude, our volunteer researchers did not expect to be attacked by a four-legged robot. Yet as the mechanical whirring and rubberized footfalls grew louder, the humans startled. The event’s organizer managed to grab hold of the robot and power it off before any damage was done to robots, tables, or human limbs. The morale of the inadvertently targeted team, however, did not escape unscathed. This dramatic event highlighted the unpredictable nature of integrating AI with physical systems.
Enterprise AI Robotics Experiment Flow
Phase One: Controller-based Operation
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Phase Two: Custom Software & Sensor Integration
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Phase Three: Autonomous Ball Detection & Retrieval
| Aspect | Team Claude (with AI) | Team Claude-less (without AI) |
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| Tasks Completed |
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| Connectivity & Sensors |
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| Control Program Development |
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| Autonomous Progress |
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| Code Volume |
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9X
More Code Written by Team Claude
Team Dynamics & Psychological Impact
The experiment revealed stark differences in team dynamics. Observers noted that Team Claude appeared significantly happier. Quantitative analysis of audio transcripts confirmed Team Claude-less expressed more negative emotion and confusion, and asked more questions, suggesting different collaboration styles when AI is present.
2X
Higher Confusion Rate for Team Claude-less
44%
More Questions Asked by Team Claude-less
Reflections on Limitations & Future Scope
Project Fetch, while insightful, had limitations: a small sample size (two teams), short duration (one day), and academically-focused tasks. Participants were Anthropic employees, potentially biasing results; AI novices might show different uplift patterns. This wasn't an end-to-end robotics test but a crucial step.
However, this experiment demonstrates significant AI uplift, where non-experts achieved difficult robotics tasks quickly. Uplift often precedes autonomy, suggesting frontier AI models will soon interact successfully with unknown hardware. Tracking AI's capability in robotics is vital, especially concerning its potential to automate and accelerate future AI development, a key threshold in Anthropic’s Responsible Scaling Policy. While timelines and physical world bottlenecks are uncertain, the idea of powerful AI systems acting via robots is becoming less "outlandish."
Estimate Your Enterprise AI ROI
Calculate the potential cost savings and hours reclaimed by integrating AI into your operational workflows, based on industry-specific efficiency gains.
Projected Annual Savings
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Annual Hours Reclaimed
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Your Enterprise AI Implementation Roadmap
Based on the experimental phases, here's a structured approach to integrating AI into your enterprise, moving from foundational setup to autonomous operations.
Phase 1: Discovery & Pilot
Initial assessment of existing workflows, identification of high-impact AI opportunities, and setup of pilot projects for hardware/software integration. Similar to familiarizing with initial robot controls.
Phase 2: Custom Integration & Data Access
Develop custom software solutions, integrate AI models with enterprise systems, and establish robust data pipelines for real-time insights and control. Mirrors building custom robot software and accessing sensors.
Phase 3: Autonomous Workflow Development
Engineer and deploy AI agents for autonomous task execution, optimize for efficiency, and establish monitoring and feedback loops for continuous improvement. Analogous to programming the robodog for autonomous fetch.
Phase 4: Scaling & Strategic Expansion
Expand successful pilot programs across the organization, adapt AI solutions to new use cases, and integrate AI into long-term strategic planning to drive sustained innovation and competitive advantage.
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