Research Strategy

Our research focuses on solving the fundamental challenge in robotics: translating human intent into effective physical action. The gap between high-level commands (“pick up the cup”) and low-level execution (joint movements, force control) has limited robotics for decades.

robotics-research

Our Intelligence Architecture

We’ve built an integrated intelligence architecture that connects human interaction to robot execution through several key layers:

Understanding Human Intent

The interaction begins with natural human commands - either through language, demonstration, or direct control. This creates a high-level representation of the task (“pick up object” or “move object from x to y”).

Vision-Language Model Intelligence Core

The central intelligence layer processes these commands through:

Perception: Understanding objects, spatial relationships, and scene context
Object Knowledge: Recognizing what objects are and their properties
Planning: Determining how to manipulate the perception/world
Spatial Knowledge: Reasoning about position, orientation, and world model generation
Action Knowledge: Connecting perceptions to appropriate action modalities

This VLM core is supported by fast-response perception algorithms that provide immediate environmental feedback.

Middle Layer Translation

The middle layer converts high-level understanding into executable robot instructions:

Task Configuration: Setting up the specific parameters of the operation
Instruction Generation: Creating detailed plans using language understanding, reasoning, and spatial landmark recognition
Task Space Representation: Converting commands into a format the robot’s control systems can process

Low-Level Control Execution

The execution layer implements the plan through specialized primitives:

Learning/Adaptation Library: Transforms task space to joint-space for different robot configurations
Manipulation Primitives: Reaching, pushing, grasping, rotating, and spinning
Navigation Primitives: Path planning and obstacle avoidance
Locomotion Primitives: Forward/backward movement, jumping, rotation

Sensing and Feedback Loop

The entire system operates as a continuous feedback loop:

Sensing: Gathering image, text, and robot state data
State Monitoring: Tracking the robot’s position and status
Environmental Interaction: Detecting changes in the environment based on robot actions
Continuous Adaptation: Adjusting plans based on real-time feedback

Key Constraints Driving Innovation

Our approach is shaped by several critical constraints:

Inference Speed

Traditional robotic systems suffer from high latency between perception and action. Our architecture addresses this through:

Separating fast perception algorithms from deeper reasoning
Using tiered processing that prioritizes immediate responses
Optimizing the interface between high-level planning and low-level execution

Cost/Efficiency Considerations

We’re designing for real-world deployment, not just research environments:

Hardware-aware algorithms that work on accessible compute
Efficient model architectures that reduce power consumption
Memory-optimized inference that runs on edge devices

Generalization Capabilities

Robots must function across diverse environments and tasks:

Architecture that transfers knowledge between different scenarios
Modular components that can be recombined for novel tasks
Learning approaches that extract general principles from specific examples

Research Directions

Our current research tackles several frontier challenges:

VLM Optimization

Current VLMs are too resource-intensive for robotics applications:

Developing smaller models that maintain critical reasoning capabilities
Exploring distillation techniques to compress internet-scale knowledge
Optimizing VRAM usage without sacrificing spatial understanding

Hybrid Intelligence Architecture

Rather than relying solely on end-to-end models:

Using VLMs primarily as task planners and configurators
Leveraging faster specialized algorithms for immediate responses
Creating efficient interfaces between reasoning and action systems

Policy Separation

End-to-end learning approaches often produce models too large for deployment:

Strategically separating high and low-level policies
Developing specialized low-level controllers that can be rapidly fine-tuned
Creating interfaces that maintain coherence between policy levels

Leveraging Internet-Pretrained Data

Efficiently transferring knowledge from large pretrained models:

Techniques for extracting actionable robotics knowledge from internet-scale VLMs
Methods for grounding language understanding in physical capabilities
Approaches to verify and correct world knowledge in robotic contexts

Cross-Platform Adaptation

Building low-level policies that work across robot configurations:

Adaptation techniques for different degrees of freedom
Abstract action representations that transfer between platforms
Learning approaches that quickly adapt to new robot embodiments

Technical Approach

We’re taking a fundamentally different approach from traditional robotics:

Intelligence-First Design: Building the brain before optimizing the body
Cross-Embodiment Architecture: Creating intelligence that works across different physical platforms
Hybrid Learning Systems: Combining the strengths of both end-to-end and modular approaches
Resource-Aware Deployment: Designing for real-world compute constraints from the start

By addressing these constraints and research challenges, we’re building robot intelligence that’s not just capable in the lab, but deployable in the real world.