Robotics
Outplaying elite table tennis players with an autonomous robot
This paper introduces Ace, the first autonomous robot capable of competing with and outperforming elite human table tennis players. Leveraging a high-speed event-based vision system, model-free reinforcement learning, and advanced robot hardware, Ace demonstrates complex, real-time interactive capabilities. The system achieved victories against elite players under official competition rules and consistently returned high-speed, high-spin shots, highlighting its potential for broader human-robot interaction applications.
Executive Impact & Business Value
Ace's groundbreaking capabilities in high-speed, real-time physical interaction offer significant implications for enterprise robotics, beyond the realm of sports.
3
Victories against Elite Players
600 rad/s
Max Angular Velocity
16.4 m/s
Max Linear Velocity
75%
Return Rate (Up to 450 rad/s spin)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
49 ms
Reaction Time to Net Contact
Ace's Control System Flow
Ball State (Position & Spin History)
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Deep RL Policy (31.25 Hz)
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Mapped to Segment Trajectory (32ms)
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Collision Check
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Execute Trajectory or Reset
| Feature | Ace (This Paper) | Previous Robots |
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| Opponent Skill Level |
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| Spin Handling |
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| Perception System |
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| Control System |
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| Real-time Interaction |
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Adaptive Response to Unexpected Events
Ace's low-latency perception and control allow for rapid adaptation to unusual shots, such as balls bouncing off the net, which are difficult to model in simulation.
Challenge: Balls hitting the net create highly unpredictable trajectories, requiring immediate and precise recalibration of robot movement – a scenario difficult to pre-program or extensively simulate.
Solution: The system dynamically diverges its trajectory based on real-time sensory input (49ms after net contact) to successfully return the ball, demonstrating robust generalization to unforeseen dynamic events beyond pre-trained simulations.
Results: Successful returns of net-contact balls, showcasing Ace's ability to adapt rapidly and safely to unexpected, dynamic events in real-time, validating the approach's generalization capability.
Calculate Your Enterprise AI ROI
Estimate the potential savings and reclaimed hours by integrating advanced AI robotics into your operations.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A typical phased approach to integrate advanced AI solutions into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Strategy
Initial consultation, feasibility study, and definition of key objectives. Identify pain points and opportunities for AI intervention.
Phase 2: Pilot & Development
Develop a proof-of-concept for a specific use case, leveraging the latest AI models and robotic hardware. Iterative testing and refinement.
Phase 3: Integration & Scaling
Seamless integration of the AI solution into existing workflows. Scale deployment across relevant departments or operational areas.
Phase 4: Optimization & Support
Continuous monitoring, performance tuning, and ongoing support to ensure long-term success and adaptation to evolving needs.
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