Models & Labs
Skild AI Uses Video to Teach Robots New Tasks
NVIDIA Bloghigh confidence
Why it matters
- →Skild AI's model reduces the need for constant retraining, saving time and resources.
- →The collaboration with NVIDIA enhances the model's adaptability and deployment in real-world scenarios.
- →This approach could transform industries by enabling robots to learn tasks quickly and efficiently.
Skild AI has introduced the S1 robot foundation model, which can learn new tasks from a single video demonstration, eliminating the need for retraining. This innovative approach, developed in collaboration with NVIDIA, uses in-context learning to enable robots to adapt to changing environments and perform complex tasks. The S1 model has shown significant improvements in task execution, with a success rate of 66% compared to 9% for similar systems. This advancement could revolutionize industries by allowing robots to quickly adapt to new tasks without extensive reprogramming.
Read originalMore from NVIDIA Blog
More in Models & Labs
Models & Labsmodels
llama.cpp b10955 release addresses heap corruption
The latest release of llama.cpp, b10955, tackles a critical issue of heap corruption by disabling the ggml-cpu precompiled header and fixing CACHE_LINE_SIZE ambiguity. This update ensures consistent CACHE_LINE_SIZE values across C++ kernels and C work-buffer sizing code, preventing heap-buffer-overflow and subsequent crashes. By restoring the natural include order and removing the std::hardware_destructive_interference_size branch, the update makes the value deterministic and include-order independent. This release is a technical fix that stabilizes the runtime environment for developers using llama.cpp.
llama.cpp Releases
Models & Labsmodels
llama.cpp b10956 release enhances SYCL backend
The latest llama.cpp release, b10956, introduces significant improvements to the SYCL backend, particularly for handling large k values in TOP_K operations. By implementing a radix select method, the update allows for efficient GPU-resident processing, avoiding previous limitations that forced operations to fall back to the CPU. This change enhances performance, especially in scenarios requiring large k values, such as qwen4exp's sparse-attention indexer. The update ensures that operations are more efficient and scalable, providing a notable boost in processing speed without regressing any measured shapes.