Blog

Mobile manipulation is inherently a holistic system with deep spatial and temporal coupling, yet our engineering capabilities force us to decompose it into modules and hierarchies. This brings a "lossy" problem. But is that the end? What can we do about it?

That infamous "Mindful Pouring" game has a level 2 that's driving everyone crazy. Instead of actually solving it like a normal person, I decided to over-engineer the problem with PDDL and Fast Downward. Because why solve puzzles manually when you can write symbolic planning domains? Also featuring: what happens when some blocks are hidden (spoiler: chaos).

In this blog, we introduce a unified Model Predictive Control (MPC) trajectory following framework for holistic mobile manipulators. Our whole-body controller coordinates both the arm and mobile base using a single optimization problem, enabling smoother trajectory following and incorporating Control Barrier Functions (CBFs) for base obstacle avoidance.

After implementing ACT, Diffusion Policy, and 3D Diffusion Policy for various manipulation tasks, I've discovered significant gaps between research papers and real-world performance. This blog examines the overfitting problems in current approaches and explores a critical question: Can scaling laws from language models apply to robotics?

Inspired by VAEs, I had this thought: "Hey, if uncertainty modeling works so well there, why not try it in other areas?" Since I'm pretty familiar with visual place recognition and noticed almost no one was doing this kind of stochastic modeling there, I thought it'd be a perfect playground for experimentation. This blog records my journey - from how I formulated the problem to what happened when I actually tried to make it work. Spoiler alert: things didn't go quite as planned, but the insights were fascinating!

This blog explores the fascinating antagonistic process in Variational Autoencoders (VAEs) between the encoder's predicted variance and decoder quality. We examine how the variance adapts throughout training, creating an automatic curriculum that balances reconstruction accuracy with latent space exploration. The post includes mathematical proofs and visualizations to illustrate this dynamic equilibrium.

This blog introduces a system for stable and consistent robot control that combines velocity control, delta pose representations, and null-space optimization. The framework ensures smooth motion generation and frame-independent movements, producing high-quality training data for advanced learning algorithms like Diffusion Policies and reinforcement learning models.