Abstract: Systems, methods, software, and devices are disclosed herein process context data to encode one or more semantic elements of a desired audio composition in a semantic token sequence, process the semantic token sequence to encode one or more structural elements of the desired audio composition in a structural token sequence disentangled from the semantic token sequence, and process the structural token sequence to encode one or more audio signal elements of the desired audio composition in an audio signal token sequence disentangled from the structural token sequence. The semantic token sequence, the structural token sequence, and the audio signal token sequence may then be processed to generate at least a portion of the desired audio composition.

Abstract: A robotic controller for controlling a robot according to a sequence of robotic actions. comprises an input interface configured to receive a plurality of multimodal inputs each specifying instructions for performing a task in a different modality including audio, video, and a text modality. The controller also comprises a multimodal large language model, an action sequence decoder, and a controller. The multimodal LLM includes a multimodal LLM encoder and an LLM decoder. The multimodal LLM encoder is trained with machine learning to transform the multimodal instructions into encodings and the LLM decoder is configured to decode the encodings into a sequence of robotic instructions. The action sequence decoder is trained with machine learning to transform the sequence of robotic instructions into a sequence of actions using a library of robotic skills. The controller is configured to control a robot according to the sequence of actions.

Abstract: A robotic controller for controlling a robot according to a sequence of robotic actions. comprises an input interface to receive multimodal inputs specifying instructions for performing a task in audio, video, and a text modality. The controller transforms the multimodal instructions into encodings using a large language model (LLM) encoder and decodes the encodings into a first sequence of robotic instructions and a robot action description of the actions using an LLM decoder. Human feedback input is received corresponding to at least one action in the first sequence of actions and the controller encodes the feedback input with the robot action description. The controller feeds the encoded data along with multimodal features generated from the encodings into the LLM decoder to generate a corrected sequence of actions. The controller is configured to control a robot according to the corrected sequence of actions.

Abstract: An audio system for synthesizing audio sounds having a desired audio trait executes an autoregressive generative audio transformer trained for generating the audio by processing inputs with multiple layers employing multi-head attention, and uses directional inference-time intervention (ITI) to push at least some outputs of at least some heads of the multi-head attention into a direction predetermined for the desired audio trait.