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Gabriel is a framework for wearable cognitive assistance using cloudlets. You can find more details about Gabriel from our design document, our paper, and our website.

Getting Started

  1. Create a Gabriel server.
  2. Create a client for Python or Android.

Example Workflows

  1. OpenRTiST
  2. Instruction-based assistants

The examples directory of this repository contains some toy workflows.


The following section provides low-level details about how this code works. See our design document for a higher-level explanation.

Clients send one frame to the server at a time. Clients have sources, which produce frames. A source can be an interactive application that sends frames without filtering them (such as OpenRTiST), or an early discard filter. Two different early discard filters can send frames that were captured by the same sensor. However, these filters are different sources from Gabriel's perspective. Each source must be given a name (such as "openrtist" or "face"). This allows cognitive engines to know what to expect in input frames and (if applicable) what results the client expects back.

Every frame from one source should have the same type of data, and this type should not change. For example, if a source sends images, it should only ever send images, and it should not also include audio along with an image. Audio and images should be sent by two different sources. InputFrame messages have an extras field that can be used to send metadata, such as GPS and IMU measurements, or app state. Embedding binary data as extras to circumvent the "one type of media per source" restriction will likely lead to cognitive engines that are difficult for other people to maintain. Multiple payloads can be sent in a single InputFrame message. This is intended for cases where an input to an engine must contain several consecutive images. A single InputFrame message should represent one single input to a cognitive engine.

Each client has one set of tokens per source. This allows the client to send frames that have passed "source x" at a different rate than it sends frames that have passed "source y." A cognitive engine can only consume frames that have passed one source. A cognitive engine cannot change the source that it consumes frames from. Multiple cognitive engines can consume frames that pass the same source.

The Gabriel server returns a token to the client for "source x" as soon as the first cognitive engine that consumes frames from "source x" returns a ResultWrapper for that frame. When a second cognitive engine that also consumes frames from "source x" returns a ResultWrapper for the same frame, the Gabriel server does not return a second token to the client. Engines can be configured to allow the server to ignore a ResultWrapper if this engine was not the first to return a ResultWrapper for a frame. Engines can also be configured to force the server to send all ResultWrapper messages to the client. When an engine configured to require all responses is not the first engine to return a ResultWrapper for a frame, the server will send the client this ResultWrapper, but it will not return a token (because it already returned the token for this frame with the result from the first cognitive engine).

Cognitive engines might not receive every frame sent to the server. In particular, the client will send frames to the server at the rate that the fastest cognitive engine can process them. Slower engines that consume frames from the same source might miss some of the frames that were given to the fastest engine for this source. After an engine finishes processing its current frame, it will be given the most recent frame that was given to the fastest engine. When the first engine completes the most recent frame, a new frame will be taken off the input queue and given to this engine.

Flow Control

Gabriel's flow control is based on tokens. When the client sends a frame to the server, this consumes a token for the source that produced the frame. When the first cognitive engine finishes processing this frame, the client gets back the token that was consumed sending the frame. This ensures that frames are sent to the server at the rate that the fastest engine consuming frames from this source can process them. If the server runs into an error processing a frame, it immediately sends a message to the client indicating the return of a token.

After a client consumes all of its tokens for a source, the client will only send a new frame from this source after it receives a token back (for this source). This can lead to periods where the server has no input when the latency between clients and the server is high. Setting a high number of tokens will fill up the queue of inputs on the server and thus reduce the length of these idle periods. However, the frames in the queue might be stale by the time they get processed. You should not set the number of tokens above two, unless the latency between clients and the server is high, and your workload is not latency critical.

Each FromClient message the client sends consumes one token. A ToClient.Response message with return_token set to true indicates the return of one token. Specifying the specific number of tokens that a client has for a source in the ToClient.Response message would lead to race conditions based on the order that the client and server send and receive messages. Representing the consumption or return of a single token in a message avoids this problem. Clients communicate with the server using The WebSocket Protocol, which uses TCP. Therefore, we assume that messages are delivered reliably and in order.

Future Improvements

  1. If two sources both send the same payload, the payload will be sent to the server twice. Caching payloads, and referencing the cached item in subsequent FromClient messages would save bandwidth.
  2. We allow multiple different cognitive engines to consume frames from the same source. However, there is no way to have multiple instances of the same engine. In particular, if there were multiple cognitive engines that performed face recognition, we would not want more than one of them to process the same frame. We need some way to decide which instance of an engine should process a given frame. For each group of engines, there should be a way to toggle between the following options:
    1. Each request can go to a different engine. There should be a scheme to load balance individual requests (such as a simple round robin). This is the best option for engines that do not store any state information. Note that if the amount of state needed for each client is small, the client and engine can pass state back and forth to each other in the extras field of InputFrame and ResultWrapper messages. This would allow the client's frames to be processed by any instance of a given engine. However, your client code needs to ignore results based on frames that the client sent before it received the latest state update.
    2. Each client is assigned to a specific instance of an engine. No other instances of this engine will get frames from this client. This setting will be used for engines that store state information for each client.
  3. Gabriel does not expose any client identification information to cognitive engines. Clients can include this information in the extras field of InputFrame messages. However, this should be added as a part of Gabriel itself at some point.
    1. Should this identity persist when the client disconnects and reconnects?
    2. If support for multiple instances of the same engine is added, should this identity be used when a group is set to assign a client to one specific instance of an engine?
  4. When cognitive engines are run separately from the server, clients do not handle the case when all engines that consume a source disconnect. To handle this case, the Gabriel server would need to tell clients when this happens. The clients would then need to stop sending inputs from this source.
  5. The security of Gabriel could be improved in a number of areas. The connections between clients and the server, and the connections between the server and standalone engine runners, could both be improved. These improvements could be in the form of encrypting traffic, requiring a password for clients and engine runners to connect to the server, and specifying a list of approved clients and engine runners in a server configuration file.