ThinkAct lets robots “think, then act” — and the payoff is new SOTA across embodied AI benchmarks

 Anyone who has watched today’s end‑to‑end robot policies fail a complex kitchen task knows the weakness: they map pixels to motors with no explicit plan. ThinkAct flips that script. The NTU‑NVIDIA team behind the paper trains a multimodal LLM to write a high‑level reasoning plan, turns that plan into a compact visual‑plan latent, then hands it to a DiT‑based action model that executes at control‑loop speed. The result is an agent that deliberates like GPT‑4o yet moves with the reactivity of classic policies.

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How ThinkAct pulls it off

Component	What it does	Why it matters
Reinforced visual latent planning	Rewards the reasoning LLM with goal‑completion and trajectory‑consistency signals derived from vision, forcing plans that actually work in the scene.	Bridges abstract language plans to pixel‑level feedback.
Visual‑plan latent	Compresses the entire chain‑of‑thought into a fixed‑size latent that conditions a frozen DiT policy.	Keeps the policy lightweight and allows asynchronous slow‑think / fast‑act loops.
Dual‑system inference	LLM thinks a few times per second; the action model ticks every 20 ms.	Yields real‑time control without sacrificing deliberation.

Benchmark sweep at two skill levels

Suite	Metric	Prev SOTA	ThinkAct
EgoPlan‑Bench2	Acc. ↑	Qwen 2.5‑VL* 66.3	71.4
RoboVQA	Acc. ↑	Qwen 2.5‑VL* 63.5	69.2
OpenEQA	Acc. ↑	OpenVLA 52.1	57.8
SimplerEnv (manip.)	Succ.% ↑	DiT‑Policy 45.2	62.7
LIBERO (manip.)	Succ.% ↑	OpenVLA 48.9	60.3

Qwen 2.5‑VL numbers are the authors’ fine‑tuned baseline.

Few‑shot powers

With just 5–10 demos per LIBERO task, ThinkAct’s policy finetunes to new objects and layouts, beating OpenVLA by 9–12 points.o

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Why this matters

• Plan‑centric embodied AI. ThinkAct shows that giving agents an explicit, reward‑aligned plan latent trumps opaque end‑to‑end policies for long‑horizon tasks.

• Self‑reflection in the loop. The reasoning LLM can detect a failure mid‑episode, revise its latent plan, and rescue the run — a first for open‑source VLA systems.

• Few‑shot deployment. Labs can adapt to a new kitchen or warehouse with handfuls of tele‑op traces instead of days of retraining.

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ThinkAct’s code is coming soon, but the project page already hosts videos of robots closing drawers, shifting condiments and answering environment‑specific questions after reasoning out loud. The message is clear: future embodied agents won’t just map images to torque — they’ll think, decide why, then act.

Paper link: arXiv 2507.16815 (PDF)

RoboBrain 2.0 aims to be the one brain your robot needs

 When you send a service bot to restock a fridge or map a disaster zone, you usually stitch together half-a-dozen neural nets: one to segment objects, another to read instructions, a planner to plot a path. RoboBrain 2.0 wants to scrap that Franken-stack and replace it with a single vision-language foundation model that can see, read, think and act. Introduced this month by Beijing Academy of Artificial Intelligence (BAAI), the system comes in two flavors—a resource-friendly 7 B-parameter variant and a flagship 32 B model—both built around a heterogenous architecture that couples a powerful vision encoder to a large-language backbone.

What’s new under the hood

Building block	Why it matters
Unified spatial + temporal training	Multistage curriculum mixes affordance prediction, spatial referring, trajectory forecasting and real-time scene-graph updates so the model learns to reason and plan.
Dense perception head	Adds point-, box- and mask-level outputs to the language decoder, letting the same network return precise coordinates without extra detectors.
Closed-loop interaction module	Keeps a rolling memory of scene changes, enabling multi-step tasks like “pick the red mug you just washed and place it on the left shelf.”

Benchmark clean-sweep

According to the technical report and accompanying GitHub data, RoboBrain 2.0-32B posts state-of-the-art or near-SOTA scores on nine spatial-reasoning suites (BLINK-Spatial, CV-Bench, EmbSpatial, RoboSpatial, RefSpatial, SAT, VSI-Bench, Where2Place, ShareRobot-Bench) and three temporal/decision-making tests (Multi-Robot-Planning, Ego-Plan2, RoboBench-Planning). That’s enough to edge past open-source front-runners like Cosmos-Reason 1 and Qwen 2.5-VL and proprietary contenders such as Gemini 2.5 Pro, o4-mini and Claude Sonnet 4.

Why those results matter

• From perception to action — in one pass. A single forward call yields language, bounding boxes and future trajectories, trimming latency for real-time robotics.

• Scales down gracefully. The 7 B version, small enough for an RTX 6000, still cracks the top tier on most spatial tasks, making embodied AI workflows feasible outside big-tech labs.

• Open weights, permissive license. Both checkpoints, training code and a new embodied-reasoning benchmark suite are already public, inviting startups to fine-tune for warehouse picking, home assistance or search-and-rescue.

The road ahead

BAAI hints that RoboBrain’s next milestones include on-device distillation for humanoid form factors and a memory-augmented planner for week-long missions. Whether the project can keep pace with multi-modal titans like Meta’s Open Sora or Google’s RT-2 remains to be seen, but RoboBrain 2.0 proves that an all-in-one “robot brain” is no longer science fiction.

Paper link: arXiv 2507.02029 (PDF)

Meta AI’s grand blueprint for embodied agents: put a world model at the core

 Move over “chatbots with arms.” Meta AI has published a sweeping manifesto that recasts embodied intelligence as a world-model problem. The 40-page paper, Embodied AI Agents: Modeling the World (July 7, 2025), is signed by a who’s-who of researchers from EPFL, Carnegie Mellon, NTU and Meta’s own labs, and argues that any meaningful agent—virtual, wearable or robotic—must learn a compact, predictive model of both the physical and the mental worlds it inhabits.

Three kinds of bodies, one cognitive engine

The authors sort today’s prototypes into three buckets:

• Virtual agents (think emotionally intelligent avatars in games or therapy apps)

• Wearable agents that live in smart glasses and coach you through daily tasks

• Robotic agents capable of general-purpose manipulation and navigation

Despite wildly different form factors, all three need the same six ingredients: multimodal perception, a physical world model, a mental model of the user, action & control, short-/long-term memory, and a planner that ties them together.

What “world modeling” actually means

Meta’s framework breaks the catch-all term into concrete modules:

1. Multimodal perception – image, video, audio and even touch encoders deliver a unified scene graph.

2. Physical world model – predicts object dynamics and plans low- to high-level actions.

3. Mental world model – tracks user goals, emotions and social context for better collaboration.

4. Memory – fixed (weights), working and external stores that support life-long learning.

The paper contends that current generative LLMs waste compute by predicting every pixel or token. Instead, Meta is experimenting with transformer-based predictive models and JEPA-style latent learning to forecast just the state abstractions an agent needs to plan long-horizon tasks.

New benchmarks to keep them honest

To measure progress, the team proposes a suite of “world-model” stress tests—from Minimal Video Pairs for perceptual prediction to CausalVQA and the WorldPrediction benchmark that evaluates high-level procedural planning. Early results show humans near-perfect and SOTA multimodal models barely above chance, highlighting the gap Meta hopes to close.

Where they’re headed next

Two research directions top the agenda:

• Embodied learning loops that pair System A (learning by passive observation) with System B (learning by physical action), each bootstrapping the other.

• Multi-agent collaboration, where a family of specialized bodies—your glasses, a kitchen robot, and a home avatar—share a common world model and negotiate tasks.

Ethics is a running theme: privacy for always-on sensors and the risk of over-anthropomorphizing robots both get dedicated sections.

Why it matters

Meta isn’t open-sourcing code here; it’s setting the intellectual agenda. By declaring world models—not ever-larger GPTs—the “missing middle” of embodied AI, the company positions itself for a future where agents must act, not just talk. Expect the next iterations of Meta’s smart-glasses assistant (and perhaps its humanoid robot partners) to lean heavily on the blueprint sketched in this paper.

Paper link: arXiv 2506.22355 (PDF)