Towards Real-World Causal Intelligence Founded by @huang_biwei
Joined April 2026
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Aether AI (Causal Intelligence) retweeted
Short-horizon manipulation is only one step toward robot autonomy.
With RobotAgentBench, we explore what happens when robots must discover tasks, reason about context, and recover from failures in open-ended scenarios. We also evaluate how agent harnesses and recursive self-improvement (RSI) can help close the gap.
Robots can now perform an increasingly wide range of manipulation tasks. But how far are we from robot assistants that can operate autonomously in the open world?
We introduce RobotAgentBench (RAB), a comprehensive benchmark for evaluating open-world robot autonomy in realistic household environments.
We evaluate 8 VLA models, 3 WAMs, and diverse robot agent harness designs across five dimensions: task discovery, context reasoning, manipulation, failure recovery, and robustness. Four open-ended household scenarios test how these capabilities work together to fulfill high-level user needs.
What we find:
- Manipulation ≠ autonomy. Standalone VLA/WAM policies struggle with task discovery and open-ended scenarios. Agent harnesses substantially improve performance, but reliable open-world autonomy remains challenging.
- Hybrid Code-VLA/WAM Policy performs best. Combining learned policies with code-based control achieves the strongest performance among evaluated configurations.
- RSI further improves robot agents. Refining reusable tools and accumulating experience lead to additional gains across iterations.
- Best couple under our default agent setting: Antigravity CLI (Gemini 4 Argon) + InternVLA-A1.5. This pairing performs best among the planner-executor combinations we evaluated, achieving an open-ended score of 10.4, compared with 8.4 for fully code-based Codex (GPT-6 Astra) and 1.1 for standalone π0.5.
Aether AI (Causal Intelligence) retweeted
Had a great time chatting with @CharlieFink and @VirtualTedS.
Reasoning before acting is a fundamental part of Physical AI. Counterfactual reasoning gives a robot a way to consider possible consequences before committing to an action.
Making that reasoning transparent is equally important if we want to understand why a particular action was chosen.
“What if I do this action? What will happen?”
That’s the core of counterfactual reasoning in Physical AI.
Our founder @huang_biwei joined @CharlieFink and @VirtualTedS on @AIXRPodcast to talk 10+ years of causal AI research, how it’s shaping our causal world models, and why Physical AI is our first proving ground.
Check out the full episode in the comments.
Aether AI (Causal Intelligence) retweeted
The hard part often comes one step earlier: how do we recover the relevant causal variables in the first place? In robotics, these variables are often not given directly. They have to be learned from inputs such as pixels, trajectories, and raw sensor streams before we can model how actions change the system.
What makes a world model causal?
As a robotic gripper closes around an object, the contact conditions change. Friction, grasp point, speed and angle can all affect the outcome.
Our framework treats causality as an explicit system of causal variables, causal structure and causal dynamics in latent space.
Which condition would you vary first?
#CausalWorldModels
Aether AI (Causal Intelligence) retweeted
This got me thinking. Scaling has brought real progress, but how far can prediction take us as AI systems move toward more general intelligence? AI needs to uncover the causal mechanisms behind patterns. Causal mechanisms, interventions, and counterfactuals are key to robust generalization, and ultimately, more general intelligence.
Judea Pearl (@yudapearl) is a Turing Award winner famous for his contributions to artificial intelligence and causal reasoning.
We talked about the greatest scientist of all time, his major breakthroughs and his predictions for AI today. Full conversation also on my YouTube, Spotify, and Apple Podcasts.
Timestamps:
00:00 Intro
00:54 How he got into AI
11:17 Greatest scientist of all time
20:15 What people thought of AI in the 80s
26:23 Entering academia and researching AI
34:52 The invention of Bayesian networks
46:28 Pioneering work in causality
55:38 The causal hierarchy
59:34 LLMs and predictions
01:20:12 A restless mind pays
01:24:36 Advice for his younger self
01:26:37 Outro