No Priors Ep. 141 | With Sunday Robotics Co-Founders Tony Zhao and Cheng Chi | No Priors: AI, Machine Learning, Tech, & Startups

In this episode of No Priors, Sarah Guo interviews Tony Zhao and Cheng Chi, co-founders of Sundae, about their work on Memo, a general home robot. They discuss the current state of AI and robotics, focusing on the shift from classical robotics to more generalizable AI algorithms. The conversation covers their research contributions, including diffusion policy, Aloha, and Umi, and how these advancements have enabled more scalable data collection and training. They delve into the design philosophy behind Sundae's home robot, emphasizing cost-effectiveness, safety, and the goal of automating mundane chores to free up people's time. They also address the challenges of scaling up robotic systems, the importance of data quality, and their plans for a beta program to gather user feedback. The discussion concludes with a look at the future impact of ubiquitous home robots and the technical skills they seek in potential team members.

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No Priors Ep. 141 | With Sunday Robotics Co-Founders Tony Zhao and Cheng Chi

No Priors: AI, Machine Learning, Tech, & Startups

Introduction to Sundae and the State of AI Robotics

The Shift from Classical Robotics to AI-Driven Robotics

Research Breakthroughs: Diffusion Policy and Imitation Learning

Aloha and ACT: Advancements in Data Collection and Dexterity

Action Chunking and the Application of Transformers to Robotics

UMI: Revolutionizing Data Collection in the Wild

From Research to Reality: The Genesis of Sundae

Building the Sundae Team and Mission

Design Philosophy: Cost, Capability, and Robustness

Simplifying Robot Design for Practicality and Affordability

AI's Impact on Actuator and Mechanical Design

Timeline for Consumer Availability and Key Learnings

The Challenges of Scaling a Robotic System

The Importance of a Full-Stack Team for Iteration

Data Collection at Scale and the Importance of Precision

Comparing Data Collection Methods: GloVe vs. Teleoperation

The Role of Reinforcement Learning (RL) in Robotics

Simulation vs. Imitation Learning: Which Method is Faster?

The Importance of Data Quality at Scale

Remaining Technical Challenges and Training Recipes

The Full-Stack Approach and Internal Collaboration

Commercial Availability and Beta Program

Cost Estimates and Future Ubiquity

The Impact of Ubiquitous Home Robots on Daily Life

Analyzing Robotics Demos: Assumptions and Generalization

Sundae's Demos: Messy Table Cleanup and Generalization

Dexterity Demos: Espresso Machine and Sock Folding

Hiring and Team Building: Full-Stack Roboticists

The Journey to Robotics and the Full-Stack Experience at Sundae

Outro

No Priors Ep. 141 | With Sunday Robotics Co-Founders Tony Zhao and Cheng Chi

No Priors: AI, Machine Learning, Tech, & Startups

00:00Introduction to Sundae and the State of AI Robotics

Introduction to Sundae and the State of AI Robotics

01:15The Shift from Classical Robotics to AI-Driven Robotics

The Shift from Classical Robotics to AI-Driven Robotics

03:13Research Breakthroughs: Diffusion Policy and Imitation Learning

Research Breakthroughs: Diffusion Policy and Imitation Learning

04:29Aloha and ACT: Advancements in Data Collection and Dexterity

Aloha and ACT: Advancements in Data Collection and Dexterity

06:14Action Chunking and the Application of Transformers to Robotics

Action Chunking and the Application of Transformers to Robotics

07:06UMI: Revolutionizing Data Collection in the Wild

UMI: Revolutionizing Data Collection in the Wild

09:11From Research to Reality: The Genesis of Sundae

From Research to Reality: The Genesis of Sundae

10:16Building the Sundae Team and Mission

Building the Sundae Team and Mission

11:30Design Philosophy: Cost, Capability, and Robustness

Design Philosophy: Cost, Capability, and Robustness

12:51Simplifying Robot Design for Practicality and Affordability

Simplifying Robot Design for Practicality and Affordability

14:03AI's Impact on Actuator and Mechanical Design

AI's Impact on Actuator and Mechanical Design

15:04Timeline for Consumer Availability and Key Learnings

Timeline for Consumer Availability and Key Learnings

16:22The Challenges of Scaling a Robotic System

The Challenges of Scaling a Robotic System

17:18The Importance of a Full-Stack Team for Iteration

The Importance of a Full-Stack Team for Iteration

18:21Data Collection at Scale and the Importance of Precision

Data Collection at Scale and the Importance of Precision

19:23Comparing Data Collection Methods: GloVe vs. Teleoperation

Comparing Data Collection Methods: GloVe vs. Teleoperation

20:28The Role of Reinforcement Learning (RL) in Robotics

The Role of Reinforcement Learning (RL) in Robotics

21:49Simulation vs. Imitation Learning: Which Method is Faster?

Simulation vs. Imitation Learning: Which Method is Faster?

23:58The Importance of Data Quality at Scale

The Importance of Data Quality at Scale

24:55Remaining Technical Challenges and Training Recipes

Remaining Technical Challenges and Training Recipes

26:22The Full-Stack Approach and Internal Collaboration

The Full-Stack Approach and Internal Collaboration

27:59Commercial Availability and Beta Program

Commercial Availability and Beta Program

29:09Cost Estimates and Future Ubiquity

Cost Estimates and Future Ubiquity

30:14The Impact of Ubiquitous Home Robots on Daily Life

The Impact of Ubiquitous Home Robots on Daily Life

31:03Analyzing Robotics Demos: Assumptions and Generalization

Analyzing Robotics Demos: Assumptions and Generalization

32:34Sundae's Demos: Messy Table Cleanup and Generalization

Sundae's Demos: Messy Table Cleanup and Generalization

35:27Dexterity Demos: Espresso Machine and Sock Folding

Dexterity Demos: Espresso Machine and Sock Folding

36:53Hiring and Team Building: Full-Stack Roboticists

Hiring and Team Building: Full-Stack Roboticists

37:45The Journey to Robotics and the Full-Stack Experience at Sundae

The Journey to Robotics and the Full-Stack Experience at Sundae

38:47Outro

Outro