ice

Robotics Engineer | Full-Stack Developer
Building AI Agents & Automation Tools
Sharing projects, code and side-business experiments

[ice @icepeak486]

@Hp2ai 第一个月的百分之十没了， 每个月500刀消费的那个就自己慢慢花吧，ai 全额报（50刀封顶）常态的 。

[ice @icepeak486]

3. Vision Training Boost: Tiled Rendering
It aggregates camera feeds from thousands of environments into a single large GPU frame buffer ("tiled" together) for direct neural network processing.

Impact: This eliminates the data bottleneck in vision-based Reinforcement Learning (RL), making the training of end-to-end foundation models feasible.
4. High Fidelity: Actuator & Complex Physics Modeling
It integrates non-linear motor models and friction models, supporting bidirectional coupling between soft bodies (cloth, ropes) and rigid bodies.

Impact: Significantly narrows the Sim-to-Real Gap, ensuring that algorithms trained in simulation perform more reliably on physical hardware.
5. Industry Standard: Powered by OpenUSD
It shifts away from traditional XML or URDF formats to fully embrace OpenUSD.

Impact: Robotic assets can now seamlessly interface with industrial-grade CAD software and cinematic rendering pipelines, unifying robotics development with professional engineering standards.

[ice @icepeak486]

Boring report， but

Here are the 5 core takeaways from the technical report on Isaac Lab:

1. Performance Peak: GPU-Native Pipeline
Unlike traditional simulators that split tasks between CPU and GPU, Isaac Lab keeps physics, sensing, reward calculation, and policy inference entirely on the GPU memory.

Impact: Training times are slashed from days to hours, enabling the simultaneous simulation of tens of thousands of robots.
2. Management Efficiency: View API & Pattern Matching
It allows developers to access state tensors for all robots using USD hierarchical paths (e.g., /World/envs/*/Robot).

Impact: Developers no longer need to manually calculate complex tensor indices, drastically reducing errors in large-scale parallel environment coding.

https://t.co/fIWCakYIxL

[ice @icepeak486]

It'a good new. best body can deliver great algorithms

https://t.co/jlFjtRqo0y

[ice @icepeak486]

think this idea should generalize beyond UEREBot.

Not just for quadruped evasion, but for many robotics systems with competing behaviors:

- planner vs reflex
- nominal controller vs recovery controller
- task policy vs safety policy
- efficiency vs robustness

The fusion strategy may be the real product.

[ice @icepeak486]

A quadruped paper I found interesting:
UEREBot：Learning safe locomotion under unstructured environments and high-speed dynamic obstacles.
What I like is not only the task.
It is the handoff design between navigation and reflexive evasion.
Source:
https://t.co/JJUvLeXPWD

[ice @icepeak486]

So my takeaway is bigger than this one paper:

For real robots, especially legged robots, we should spend more time on policy transition design instead of only asking whether each individual policy is good.

Sometimes the key innovation is not a better policy.

It is a better way to blend policies safely.

[ice @icepeak486]

The paper’s ablation supports this.

Replacing the fusion handoff with a hard handoff hurts task success and increases locomotion cost.

That matches a broader robotics lesson:

A multi-policy system is only as good as its transition logic.

[ice @icepeak486]

For quadrupeds, this matters a lot.

A hard switch may be logically correct at the behavior level, but still bad at the locomotion level.

Why?

Because abrupt command discontinuities can create unstable transients:
- bad velocity tracking
- awkward posture changes
- extra corrective motion
- lower overall robustness

[ice @icepeak486]

This is the part that reminds me a bit of MoE.

Not in the strict LLM sense.

But in the sense that different expert behaviors exist:
- progress-tracking
- reflexive evasion

And the system needs a gating / fusion mechanism to decide how much each one should influence the final command.

[ice @icepeak486]

What I find most interesting is the threat-aware handoff.

Instead of hard-switching between navigation and reflex actions, they use a learned fusion function driven by a threat score.

So the command can move smoothly from “track the path” to “evade now”.

That is a strong systems idea.

[ice @icepeak486]

The core problem is real:

A navigation policy wants progress.
A reflex policy wants immediate safety.

Under fast dynamic obstacles, those objectives can conflict.

If you switch too slowly, you get hit.
If you switch too aggressively, you break locomotion continuity.

[ice @icepeak486]

The system is built as a hierarchy:

- a spatial-temporal planner
- a navigation policy for goal progress
- a reflex policy for fast evasive response
- a threat-aware handoff
- a CBF shield as the final safeguard

That decomposition makes a lot of sense for legged robots.

[ice @icepeak486]

Interesting demo. I like this game

[Pengcheng Wang @Outsider_pc]

Real-time Chunking (RTC) is designed to enable smooth asynchronous execution of flow-matching policies. However, it has some critical limitations: its inpainting-based async execution capability comes from inference-time corrections rather than the base policy, yielding little pre-training benefit, specific fine-tuning for better performance (e.g. training-time RTC), heuristic guidance, and extra computation that inflates the latency.

In this work, we observe that discrete diffusion policies, which generate actions by iteratively unmasking, are natural asynchronous executors that resolve all limitations at once, being simpler to implement, faster at inference, and better at execution.

Paper: https://t.co/SbpNCQMBF1
Code: https://t.co/fQyT6NR2eE
Website: https://t.co/jLiCogvxoa

[ice @icepeak486]

Mark

[Ilir Aliu @IlirAliu_]

ETH Zurich just open-sourced their entire 2026 robot learning course.

Not a MOOC. The actual course. Slides, lecture recordings, coding assignments, GitHub repo.

The curriculum goes from imitation learning and RL all the way to Vision-Language-Action models and foundation models for robotics.

Guest lectures from the co-founder of Physical Intelligence. The creator of Diffusion Policy. Pieter Abbeel. Dieter Fox.

12 weeks. Free. No signup.

If you want to understand where robot intelligence is actually heading… this is the reading list the field is using right now.

📍[https://t.co/eKsIjILi60]

——

Weekly robotics and AI insights.
Subscribe free: https://t.co/9Nm01QUcw3

[ice @icepeak486]

Ar/vr can have some real progrees. Still need fair price and appropriate weight。

[Coin Shot ☁️ @CoinSh0t]

This girl alone crashed the stocks of Figma and Photoshop.

Her new technology "touchdesign" made her $420,000

It's already used in Nolan's "Oddysey" movie.

She had the idea, and Claude built it from scratch.

I reverse-engineered this and was shocked by how easy it was to recreate:

The whole system runs on your laptop with just a webcam and two free programs.

MediaPipe reads your webcam and tracks twenty-one points on each hand.

TouchDesigner turns those points into a 3D object that reacts to every move you make.

1. Download TouchDesigner from https://t.co/u5ttgWK7FJ with the free non-commercial license.

2. Grab the MediaPipe plugin from https://t.co/29A1w2XzxL and drag the file into TouchDesigner, no installation needed.

3. Click the hand_tracking node and set numHands to 2 so both your hands get tracked in real time.

4. Open Claude Code in the project folder and paste this prompt:

{ Write me a TouchDesigner Python script that creates a 3D glitchy
cube controlled by both hands using the MediaPipe plugin.

Controls:

- Midpoint between both hands moves the cube around the screen

- Angle between the hands rotates the cube on X and Y axes

- Distance between both hands scales the cube up or down

- Left-hand pinch increases the noise distortion

- Right-hand pinch cycles through colors

- Fast movement triggers a glitch flash

- Both fists freeze the frame for a screenshot

Output one Python DAT script with comments so I can tune each value. }

5. Paste the script into a Python DAT node and wire the MediaPipe outputs into it.

6. Press Play and the cube appears floating in front of you, moving with your hands.

7. Swap the cube for any other object later, and all the hand controls keep working the same way.

The girl performs these sessions live at fashion shows and launches.

She charges five to fifteen thousand dollars per event.

She also sells preset packs on Gumroad for ninety-nine dollars each.

Her webcam cost eighty dollars and her software cost nothing.

The whole effect was built in one weekend with Claude.

[ice @icepeak486]

这个讨论希望发酵下，现在vibe软件太容易了。需要一波社会培训。

[Xuanwo @OnlyXuanwo]

其实我觉得 @HiTw93 这个推和对应的 Issue 里面的提法是很好的，我建议有类似困扰的同学都学习一下：

- 首先，MIT 协议开源的项目确实可以被用来做商业项目，@HiTw93 明确承认了这一点，赞
- 但是 MIT 协议开源并没有授予使用对应项目品牌的权利，也就是说不能使用原项目的品牌来为自己做宣传，也不能说自己是 xxx 的免费开源替代，这是明确的侵权行为。
- @HiTw93 在 issue 中提出来的删除 mole 商业产品的 reference 和 UI 相关争议诉求也非常的合理

所以未来产生了相关的纠纷，请务必搞清楚自己的权利的边界。

最后的最后：

- 如果想要构建一个商业项目，请务必想清楚如果有人做了同样的竞品自己该怎么做。AI 时代这件事情的发生概率已经是 100% 了，不要觉得自己的项目小或者够垂直就不会有人这么做
- 在复制一个项目成本几乎为零的时候，更加要想清楚自己的特点，优势和品味在哪里，不要无脑复制热门项目，烧了无数 token 之后发现自己一无所获

[ice @icepeak486]

@Hp2ai @grok fps是啥

[ice @icepeak486]

@smykx Learn 👍